The impact of climate and CO2 change on potential winter
wheat yields in the Netherlands from 1981 to 2010
Abram het Lam
MSc thesis Plant Production Systems
February 2014
The impact of climate and CO2 on potential winter wheat yields in the
Netherlands from 1981 to 2010
Abram het Lam
MSc thesis Plant Production Systems
PPS80436
February 2014
Supervisors:
prof.dr.ir. M.K. van Ittersum
ing. H.C.A. Rijk
Examiners:
dr.ir.ing. A.G.T. Schut
Foreword This research is carried out in the context of my MSc program ‘Plant Sciences’ at the faculty of Plant
Production Systems of Wageningen University.
The research is based on modelling work. Although modelling might seem quite impersonal work, I
had a quite intense relationship with the LINTUL model. If it worked I was very excited about its
output, although sometimes it turned out there was something wrong beneath the shiny top layer of
the results. There were, however, also many times when it did not do what I wanted and I did not
know what was wrong. In the end we found a compromise, LINTUL gave me outputs and I accepted
not everything was perfect. In the mean time we have shared a wedding, a move and an internship.
Since the research is at its end, I would like to thank the people around me who supported me during
this adventure. Firstly, I would like to thank my supervisors Martin van Ittersum and Bert Rijk for
their time, support and guidance which kept me sharp and close to reality. I would like to thank Joost
Wolf as well, for his fast and clear support regarding the LINTUL model. Of course I am also very
grateful to my wife, Sanne, for her support and patience during the process. Furthermore, I much
appreciated the quiet and atmospheric working place, provided by PPS. Finally, I would like to thank
my examiners for the time and interest they invest in my work.
Kind regards,
Abram
Contents Foreword ................................................................................................................................................. 3
Contents .................................................................................................................................................. 4
Summary ................................................................................................................................................. 6
1 Introduction ...................................................................................................................................... 8
2 Materials and Methods ................................................................................................................... 12
2.1 Regression analyses ............................................................................................................... 12
2.1.1 Yield ................................................................................................................................... 12
2.1.2 Area ................................................................................................................................... 12
2.1.3 Climate ............................................................................................................................... 12
2.1.4 Correlation between yield and climate ............................................................................. 14
2.2 Modelling ............................................................................................................................... 15
2.2.1 The LINTUL model ............................................................................................................. 15
2.2.2 Calibration of the model.................................................................................................... 20
2.2.3 Validation........................................................................................................................... 23
2.2.4 Model for current varieties ............................................................................................... 23
2.2.5 Model limitations .............................................................................................................. 23
2.2.6 Simulation .......................................................................................................................... 24
3 Results ............................................................................................................................................. 26
3.1 Area and yield analyses ......................................................................................................... 26
3.1.1 Area ................................................................................................................................... 26
3.1.2 Yields .................................................................................................................................. 26
3.2 Weather and CO2 trends ....................................................................................................... 28
3.2.1 CO2 ..................................................................................................................................... 28
3.2.2 Weather ............................................................................................................................. 28
3.3 Correlation between winter wheat yields and weather factors and CO2 ............................. 30
3.3.1 Correlations between factors ............................................................................................ 30
3.3.2 Models with combined factors .......................................................................................... 30
3.4 Calibration ............................................................................................................................. 33
3.4.1 Development ..................................................................................................................... 33
3.4.2 Growth ............................................................................................................................... 35
3.5 Simulations ............................................................................................................................ 38
3.5.1 Influence of temperature change...................................................................................... 38
3.5.2 Influence of radiation change ............................................................................................ 38
3.5.3 Influence of CO2 change..................................................................................................... 42
3.5.4 Influence of actual weather .............................................................................................. 42
4 Discussion ........................................................................................................................................ 44
4.1 Yield ....................................................................................................................................... 44
4.2 Area ....................................................................................................................................... 45
4.3 Climate effects ....................................................................................................................... 46
4.3.1 Changes in climate ............................................................................................................. 46
4.3.2 Effects of changes in climate ............................................................................................. 47
4.3.3 Modelling ........................................................................................................................... 48
4.3.4 CO2 effect ........................................................................................................................... 51
4.3.5 Combined effect ................................................................................................................ 52
4.4 Drivers of yield trends ........................................................................................................... 52
4.4.1 Extreme events .................................................................................................................. 53
4.4.2 Ozone ................................................................................................................................. 54
4.4.3 UV-B ................................................................................................................................... 55
4.4.4 Management ..................................................................................................................... 55
5 Conclusions & Recommendations ................................................................................................... 57
5.1 Main findings ......................................................................................................................... 57
5.2 Recommendations................................................................................................................. 57
6 References ....................................................................................................................................... 59
7 Appendices ...................................................................................................................................... 64
Appendix I Results .............................................................................................................................. 65
Appendix II .......................................................................................................................................... 88
Appendix III ......................................................................................................................................... 89
6
Summary There are indications that winter wheat (Triticum aestivum L.) yields in North-western Europe are
stagnating since the 1990’s and that yield gaps are increasing. This will affect regional food
production and farm income. In the Netherlands genetic potential yields of winter wheat are still
linearly increasing since the 1980’s, however farm yields are not keeping up with the increase in
potential yields of variety trials. Reasons for the increasing yield gap might be changes in
environmental factors, including climate, or management. This research is aimed at unravelling the
influence of climate and CO2 on winter wheat yields from 1981 to 2010, using crop modelling.
Trends in yields and areas of winter wheat in the Netherlands were analysed using Genstat 14. Daily
weather data on average temperature, precipitation, incoming global radiation and reference
evapotranspiration and CO2 were analysed for trends. Furthermore, regression analyses were carried
out on the relation between climatic factors and winter wheat yields from 1981 to 2010. Potential
yields of winter wheat from 1981 to 2010 were simulated with the LINTUL1 model based on
temperature, radiation and CO2, separate and combined, for normal (10 October) and late sown (25
November) winter wheat based on current ‘varieties’ and varieties of the early 1980’s. Changes in
potential yields were analysed, based on separate climate factors as well as all climate factors
together.
There were no changes in winter wheat area. A linear increase in winter wheat yields of 66.3 kg ha-1
year-1 was found for the period of 1981 to 2010 and quadratic increases were found for periods
starting earlier than 1978 to 2010. CO2 concentrations in the air increased from 340 to 390 ppm from
1981 to 2010. For all stations analysed, average temperature during the growing season, from April
to July and from June to July increased linearly with 1.5, 1.83 and 0.725 oC in 30 years, respectively.
Incoming global radiation over the growing season and from April to July, increased linearly with ±
250 MJ m-2 in 30 years. Reference and actual evapotranspiration also increased linearly, with ± 50
mm over 30 years. Cumulative rainfall and precipitation deficit from April or June to July did not
show significant trends due to large annual variability. Winter wheat yields were negatively
correlated with average temperature over the growing season and rainfall from April to July. Positive
correlations with yields were found with precipitation deficits from April and June to July. Simulation
results show that temperature had no significant effect on potential winter wheat yields. Radiation
and CO2 both had a positive effect on wheat yields, with average increases of 10% and 20%
respectively. The effect of CO2 was non-linear, with diminishing increase with higher CO2 towards the
end of the analysed period. In this research, these climate factors together lead to an overall increase
of 31% in potential winter wheat yields, with the same non-linearity as found for CO2. The harvest
index of winter wheat declined from 0.51 to 0.47 on average, due to sink limitation of the grain.
The increase in potential yields due to changed weather together with genetic yields improvements
are not fully reflected in farm yields, indicating that there must be other (negative) influences. Many
factors could be responsible for this negative impact, including changes in extreme weather events,
UV-B radiation and management related issues. Management could have changed due to changing
regulations and other socioeconomic influences such as cereal prices. Changes in soil compaction,
fertilization, timing of management and extensification might also result in reduced yields. Further
research should aim at exploring these influences in more detail.
8
1 Introduction
‘How to feed the world?’ is one of the main questions in agronomic science these days. Due to fast
population growth, 9 billion people are expected to live on this planet in the year 2050 (FAO, 2002;
Godfray et al., 2010; FAO, 2009). Because of increasing wealth of people in developing countries,
dietary demands of the world population will change to more meat based meals (Godfray et al.,
2010; Spiertz and Ewert, 2009). Besides this, the meat that is consumed is shifting from ruminants,
which are fed with grassland products, to non-ruminants, which are fed with arable crop products
(Koning et al., 2008). These changes in human diets will amplify the increase in crop production
demand from arable land (Godfray et al., 2010; Spiertz and Ewert, 2009; Jaggard et al., 2010). Taking
all effects into account, 50 to 100% more food will be needed halfway this century (Godfray et al.,
2010; Jaggard et al., 2010; FAO, 2009). Increasing interest in the growing of biofuel crops as an
alternative energy source to fossil fuels, growing costs of fossil fuel supply and a growing energy
demand from developing countries, put even more pressure on agricultural production (Godfray et
al., 2010; Gregory and George, 2011; Spiertz and Ewert, 2009; Tweeten and Thompson, 2008).
In addition to a growing demand for production, the agricultural sector faces other challenges. Due
to climate change more and more extreme weather events occur all around the world (Gardner,
2010; Krugman, 2011; Peters, 2011; Romm, 2011; Spiertz and Ewert, 2009; Tweeten and Thompson,
2008). During the second half of the first decade of this century, global food production has been
significantly affected by droughts in Australia (Spiertz and Ewert, 2009; Tweeten and Thompson,
2008), Russia, China and Brazil and flooding in Australia, Brazil, and Pakistan (Gardner, 2010;
Krugman, 2011; Peters, 2011; Romm, 2011).
Although only about half of the 4.4 billion hectare of land suitable for agriculture on earth is currently
under arable cultivation (Part et al., 2011; Fischer et al., 2011), expansion of this cultivated area is not
a desirable strategy to increase crop food and feed production. Firstly, because the currently
cultivated land is the most productive land that is available (Fischer et al., 2011). So, increasing the
total crop production by cultivating extra land will take more and more effort and inputs. Secondly,
most of the well suited land for arable production is currently under forest, grassland or woodland,
which are inhabiting a vast spectrum of biological life (Fischer et al., 2011). According to Dobrovolski
et al. (2011) and Prins et al. (2011), agricultural expansion is the main cause of biodiversity loss,
mainly due to habitat loss. Finally, because of expansion of cities, the growth of other land use
activities like recreation and losses of high quality agricultural land due to degradation, options for
expansion of food and feed production area are limited even more (Godfray et al., 2010; Tweeten
and Thompson, 2008). While, Gregory & George (2011) estimate that only 20% of the total increase
in food production will come from expanding the area of crop production, Mandemaker et al. (2011)
warn that more expansion will occur, if global yields are not improved. Therefore increasing the
productivity of the current limited agricultural area is crucial to meet the growing demand for
agricultural products without biodiversity loss (Godfray et al., 2010; Jaggard et al., 2010; Spiertz and
Ewert, 2009).
Tweeten & Tompson (2008), report that cereals make up half of global human diets and two-third if
animal feed is included. Therefore cereal yield improvement is essential for increasing food
production overall (Fischer and Edmeades, 2010). In the second half of the twentieth century, indeed
a steady growth in average yields for the main cereal food crops was found (Fischer and Edmeades,
9
2010; Calderini and Slafer, 1998). This growth was fuelled by improving genetic potential of cereal
crops via breeding, as well as increasing use of inputs, like fertilizers, irrigation and crop protection
agents (Calderini and Slafer, 1998; Gervois et al., 2008). Recently however, several researches point
out that this growth is stagnating for some of the major cereal crops in several countries around the
world (Brisson et al., 2010; Calderini and Slafer, 1998). Calderini & Slafer (1998) report that there was
no yield improvement for wheat (Triticum aestivum L.) in Japan, USA, Canada, Tunisia, France and
the UK and even a decline in former USSR and in Spain in the period of 1985 to 1997. In agreement
with this, Slafer & Peltonen-Sainio (2001), Peltonen-Sainio (2009), Brisson et al. (2010) and Finger
(2010) show that growth in wheat yields in a large part of Europe stagnates since the mid-nineties of
the previous century.
Since the stagnation in wheat yield increments in Europe became clear, it has been suggested that
potential yields cannot be improved much more since a ceiling in genetic potential of the crop is
reached or approached via breeding (Jaggard et al., 2010; Calderini and Slafer, 1998). The idea
behind this is, that the biggest gains in yield come from an increased harvest index and the optimum
harvest index has almost been reached for most crops (Jaggard et al., 2010; Brisson et al., 2010;
Fischer and Edmeades, 2010; Peltonen-Sainio et al., 2009). Nevertheless, there are still a lot of other
traits that can be improved, for instance, light use efficiency or phenology (Jaggard et al., 2010;
Fischer and Edmeades, 2010; Spiertz and Ewert, 2009; Godfray et al., 2010; Peltonen-Sainio et al.,
2009). Trends in genetic potential of crops also show that in most cases there is still a linear genetic
progress in potential yields (Peltonen-Sainio et al., 2009; Brisson et al., 2010; Fischer and Edmeades,
2010). For winter wheat in the Netherlands, Rijk et al. (2013) found an average linear genetic yield
increase of 0.09 Mg ha-1 year-1 on marine clay. This is in accordance with results from Finland
(Peltonen-Sainio et al., 2009), France (Brisson et al., 2010) and the UK (Fischer and Edmeades, 2010),
although for Finland the magnitude of increase was much smaller i.e. 17 to 46 kg ha-1 year-1. So,
despite some suggestions, it is clear that the increase in genetic potential of wheat in the
Netherlands is not reaching a ceiling at this moment.
In practice, crop yields are determined by the interaction between three main factors. Namely, the
genotype of the crop (G), the environmental conditions in which the crop grows (E) and the
agricultural management that is imposed on the crop (M) (Messina et al., 2009; Loomis and Connor,
1992; Cooper and Hammer, 1996). Although management or agronomy is actually affecting the
environment of the crop, it is useful to separate E and M (Cooper and Hammer, 1996). (E) is then
considered the part of the environment that cannot be adapted easily, while (M) is the part that can
be adapted. Wheat yields are thus changing by changes in one or more of the factors in the GxExM
interaction.
In order to minimize the yield gap it is important to understand the aspects that determine current
yield levels. Van Ittersum & Rabbinge (1997), divide these aspects into three main categories. Firstly,
growth defining factors, which determine the potential yield if resources are optimally supplied and
there are no growth reducing pests, diseases or weeds. These factors include: incoming solar
radiation; temperature; CO2 levels; and genetic features of the crop variety. Secondly, growth
limiting factors including: available water and nutrients, which determine water and nutrient limited
crop yields. Thirdly, growth reducing factors, i.e. pests, diseases and weeds which reduce crop yields
if they are active, these result in actual yields on farms. Yield levels can be improved by making
adaptations to any factor in one of the three categories (Van Ittersum and Rabbinge, 1997). To close
10
the gap between actual yields and nutrient and water limited yields, pest and weed management,
seeding time, crop rotation and soil management must be improved. The gap between nutrient and
water limited and potential yields can be reduced by optimizing resource supply. Finally, also
potential yields can be improved, by breeding crop varieties with a higher yield potential under the
given biophysical conditions.
If the yield gap between potential and actual yields is increasing and genetic potential yield (G) of
winter wheat is not increasing slower than actual yield, it means that E or M are limiting or reducing
yield increase on-farm. Due to global warming, important environmental factors are changing and
predicted to change even more. Temperature, incoming solar radiation, CO2 concentrations in the air
and rainfall are the most important factors that change (Jaggard et al., 2010; Olesen and Bindi, 2002;
Brisson et al., 2010). It could thus be, that climate change reduces wheat yields, directly by altering
growth and development of the crop or indirectly by changing the effect of pests and diseases or
management (Godfray et al., 2010; Jaggard et al., 2010; Spiertz and Ewert, 2009; Prins et al., 2011;
Peltonen-Sainio et al., 2009; Olesen and Bindi, 2002; Gervois et al., 2008). Besides this, changes in
crop management (M) by farmers might be a reason for stagnating yields (Brisson et al., 2010; Van
Ittersum and Rabbinge, 1997; Peltonen-Sainio et al., 2009; Gervois et al., 2008; Hanse, 2011).
According to Brisson et al. (2010), the stagnation in Netherlands started after 1993. This trend was
not found by Rijk et al. (2013), however the latter found a decrease in realization of potential yields
gains at farm level, meaning an increasing yield gap. This increasing yield gap at farm level is not only
harmful for regional food supply, but can also be negative for farmer income.
To be able to steer future research and other efforts to reduce the yield gap of winter wheat, it is
important to clarify what the exact causes for the widening yield gap are in the Netherlands.
Therefore the effect of climate change on average winter wheat yields in the Netherlands from 1981
until now has been investigated. To this end data on average national winter wheat yields has been
analysed and the effect of climate change on wheat yields evaluated, using a crop growth model.
Since the cultivated area with winter wheat in the Netherlands is the largest of all grain crops, this is
focused on winter wheat only.
Research questions
The main question addressed in this research is: What is the effect of climate change on winter wheat
yields in the Netherlands from 1981 until 2010? This main question has been addressed via two sub-
questions. Firstly the trends in national and regional winter wheat yields in the Netherlands from
1981 until 2010 have been investigated. Secondly the effect of climatic conditions on national an
regional wheat yields in the Netherlands from 1981 until 2010 has been explored in three steps:
Is there a significant trend in CO2 concentration in the air, daily incoming solar radiation and
daily average temperatures during the growing season and in the total amount of rainfall in
the period from April to July, over the period of 1981 - 2010?
Is there a significant correlation between winter wheat yields and CO2 concentration in the
air, total incoming solar radiation and daily average temperatures during the growing
11
season and in the total rainfall deficit in the period from April till July, over the period of
1981 - 2010?
Are simulated winter wheat yields with measured climate data showing significant trends in
wheat yields over the period of 1981 - 2010?
12
2 Materials and Methods
2.1 Regression analyses
2.1.1 Yield
Data of national yields from 1970 to 2010 and regional winter wheat yields from 1981 to 2010 was
retrieved from the “Dutch Agricultural Economics Institute Foundation” (LEI) and “Statistics
Netherlands” (CBS) (CBS and LEI; LEI and CBS). The moisture content of the grain was 16%. Regional
yields were based on Dutch agricultural regions as classified by (CBS) (Fig. 3.1). The classification of
the agricultural regions was changed by the CBS between the years 1990 and 1991 (Fig. 3.1). Yield
records for similar areas from before and after the reclassification were merged if it was plausible
that the change did not affect yield trends. This plausibility was evaluated based on the overlap of
regions, differences in dominant soils types between old and new regions and the continuity of yield
trends before and after the switch. The following regions were used for the yield trend analyses:
‘Oostelijk veehouderijgebied’, ‘Centraal veehouderijgebied’, ‘Zuidelijk veehouderijgebied’,
‘Rivierengebied’, ‘IJsselmeerpolders’, ‘Zuidwestelijk akkerbouwgebied’ and ‘Zuid-Limburg’.
Trends in the national and regional yields were evaluated with linear, quadratic, exponential and
broken stick trend models, to select the best fitting model. Linear and quadratic models were
compared based on the F probability (P < 0.05) of change between the models with the linear
regression test, where a quadratic model was only used if significantly better. In addition to that, all
four models were compared on the basis of the adjusted R2. The statistical software program Genstat
14th edition (Payne et al., 2011) was used.
2.1.2 Area
The national and regional acreage of winter wheat in the Netherlands from 1980 to 2010 was
collected from the LEI (CBS) (CBS and LEI; LEI and CBS). As with yield data the areas were fit to linear
and quadratic models to investigate historical changes which could lead to changes in average yields.
Area data was available for all regions in the Netherlands. Therefore the regions ‘Hollands/Utrechts
weidegebied’,’ Waterland & Droogmakerijen’, ‘Bouwhoek & Hogeland’, ‘Veenkoloniën & Oldtambt’,
‘Westelijk Holland’ and ‘Zuid-West Brabant’ were also included in the regression analyses.
2.1.3 Climate
Daily values of incoming radiation, minimum and maximum temperatures, and amounts of rainfall
during the growing season (October – July) and monthly Makkink reference evapotranspiration from
April to July in the period from 1980 up to and including 2010 were downloaded from the Royal
Netherlands Meteorological Institute (KNMI) (KNMI, 2012) for all meteorological stations in the
Netherlands. For only four stations, ’De Bilt’, ‘Eelde’, ‘Vlissingen’ and ‘Maastricht’, the time series
from 1981 until 2010 were complete for all climate indicators. To get a better spatial coverage of the
country, data from station ‘Twenthe’ was added. For this station data on global radiation and
reference evapotranspiration before 1988 was missing. Monthly atmospheric CO2 concentrations
were collected from the ESRL Global Monitoring Division of the National Oceanic & Atmospheric
Administration in the USA (ESRL, 2012).
13
Figure 3.1 Classification of Agricultural regions in the Netherlands until (A) and after (B) 1990 according to CBS.
A B
14
To analyse the trends in the different climate factors, they were aggregated into a suitable temporal
format. Since crop growth model LINTUL, which will be used, is not designed for inserting monthly
CO2 values and trends in CO2 over different years not within years are assumed to be important, the
average CO2 concentration over the whole year was calculated from monthly values. Daily average
temperatures were calculated by dividing the sum of the minimum and maximum temperature per
day by two. To see the effect of average temperature on plant development as a whole, these values
were averaged over the growing season, mid-October to mid-July. In addition to this the average
temperatures from mid-June to mid-July and from mid-April to mid-July were computed to explore
the effect on heat stress during grain filling. Incoming solar radiation was accumulated for the entire
growing season. Monthly rainfall and Makkink reference evapotranspiration were accumulated for
the period of the season where water shortage can be limiting plant growth (mid-April – mid-July).
The actual evapotranspiration of wheat was estimated by combining reference evapotranspiration
with the wheat crop factors provided by KNMI (Hooghart, 1988). With the actual evapotranspiration
and rainfall data the precipitation deficit was calculated on a monthly basis.
2.1.4 Correlation between yield and climate
In order to find out which climate factors were important for simulating winter wheat yields in the
Netherlands based on the environment, and should thus be included in the model, it was necessary
to investigate if they have significantly influenced winter wheat yields in the Netherlands from 1981
until 2010 and in which part of the growing season.
Therefore, the correlation between the different climate factors and wheat yields and interaction
effects between climate factors on wheat yields from 1981 to 2010 were investigated with Genstat
14th edition at regional and national scale. National yields were linked to the edited climate data from
weather station ‘De Bilt’, since this is the central and main station of the KNMI. Besides national
yields also the regional yields of ‘Oostelijk veehouderijgebied’, ‘Centraal veehouderijgebied’,
‘Zuidelijk veehouderijgebied’ ‘Hollands/Utrechts weidegebied’, ‘Waterland en droogmakerijen’ en
‘IJsselmeerpolders’ were linked to ‘De Bilt’, since this is the closest station with a complete dataset
for the whole period. For the same reason the yields from ‘Zuid-Limburg’ and ‘Zuidwestelijk
akkerbouwgebied’ were linked to edited meteorological data from the stations ‘Maastricht’ and
‘Vlissingen’, respectively.
A linear model including all climate factors that significantly influenced wheat yields was composed
using bidirectional elimination, which is a combination of backward elimination and forward
selection. This method of selection was used since some factors were interrelated so both dropping
and adding should be included to give all factors a ‘chance’ to be included. For this a multivariate
regression model was used, e.q. Y = a + b.X1 + c.X2 + ...
15
Figure 3.2 Schematic overview of the main inputs, processes and outputs of the basic LINTUL1 programme,
as described by Van Oijen & Leffelaar (2008a). This picture is a simplification of the real model.
2.2 Modelling Since the correlation analyses showed that average regional and national winter wheat yields in the
Netherlands from 1981 until 2010 were not significantly influenced by precipitation deficit (see
results 3.4.2), rainfall is not included in the modelling in this research.
2.2.1 The LINTUL model
The model used in this study is the Light Interception and Utilization (LINTUL1) model (Spitters,
1990), which is a dynamic and deterministic physiological model that calculates potential crop
growth. The model is based on the LINTUL1 version as described by Van Oijen & Leffelaar (2008a).
This model simulates crop yield based on two main processes, i.e. crop development and radiation
driven growth.
2.2.1.1 Development
The timing of flowering/anthesis and harvest/ maturity are calculated with a different temperature
sum, based on a base temperature (Tbase) for development and the daily average temperature (fig.
3.2). Development starts if the set day of emergence is reached. The accumulated temperature
above Tbase determines the development stage (DVS) of the crop. By definition nthesis occurs when
DVS is 1 and the growing cycle of the crop is finished when DVS reaches 2.
2.2.1.2 Growth
From the day of emergence onwards, the wheat plants start producing assimilates; the production
depends on the daily amount of photosynthetically active radiation (PAR), the leaf area index (LAI)
and the light use efficiency (LUE). During early development stages, the growth of the LAI is mostly
constrained by the daily temperature since this is the limiting factor. Later on the increase in LAI is
determined by growth of leaf biomass. During the growing season leaves will die off. Firstly because
of shading above a certain LAI, and secondly because of ageing of the leaves if the end of the growing
season approaches. The growth of leaves and of the other plant organs (stems, roots and grains) is
determined by the total amount of assimilates produced and the fraction of these assimilates which
is allocated to the organ in consideration. The fraction of allocation to the organs depends on the
development stage of the plant. After anthesis all assimilates are allocated to the grains. If
development stops, growth of the plants also stops.
OU
TPU
TS
Stem
Development
Leaf Area Index
Assimilate production
Leaves
Assimilateallocation
INPUTS
Temperature (minimum – maximum)
Photosynthethically Active Radiation
Roots Grain
16
2.2.1.3 Model extensions
Emergence
To simulate the time of emergence of winter wheat, a new module was included. The following
formula is describes this module:
∫( )
The time of emergence occurs if the temperature sum for emergence (Tsume) is equal to the required
temperature sum for emergence (Tsum-em).
Tsume is calculated by taking the integral of
the soil temperature (Tsoil) minus the base
temperature for emergence (Tbasem). Given
that the day of sowing has passed and that
Tsoil > Tbasem. The LINTUL script for this is
shown in Box 3.1.
Soil temperature
In order to determine the date of emergence with the described procedure, the soil temperature has
to be calculated. This is done in a new subroutine in the LINTUL model, based on a formula
developed by Zheng et al. (1993).
( )
The soil temperature is calculated on the basis of the daily average temperature (Ta) and a resistance
factor (M) for the flow of energy between the air and the soil. The daily soil temperature (Tsoil) is the
temperature of the previous day (Tsoil [t - 1]) plus the difference between that temperature and the air
temperature on the current day (Ta) times the resistance factor (M). The conversion factor is
introduced because soil temperature fluctuates less than the air temperature. The seeding depth is
assumed to be constant See Box 3.2 for the LINTUL script.
Vernalization and Photosensitivity
Since the model described by Van Oijen & Leffelaar (2008a) is a spring wheat model, vernalization
and photosensitivity processes had to be incorporated to simulate development of winter wheat.
These processes were already put into mathematical formulas by Van Bussel et al. (2011) (See. Fig.
3.3)
Box 3.1: The emergence procedure
INCON TSUMEI = 0.
TSUME = INTGRL(TSUMEI, RTSUME)
RTSUME = DTEFFEM * SOWN
DTEFFEM = MAX( 0.,SOILTMP-TBASEM )
PARAM TSUMEM = 89.
PARAM TBASEM = 1.3
EMERG = INSW(TSUME-TSUMEM, 0., 1.)
17
Box 3.2: The soil temperature procedure
SOILTMPI = DAVTMP
SOILTMP = INTGRL(ZERO,RSOILTMP)
DEFINE_CALL SOILTEMP(INPUT,INPUT,INPUT,INPUT,INPUT,INPUT, OUTPUT)
CALL SOILTEMP(SOWN,SOILTMPI,SOILTMP,DAVTMP,MSOIL,DELT,...
RSOILTMP)
SUBROUTINE SOILTEMP(SOWN,SOILTMPI,SOILTMP,DAVTMP,MSOIL,DELT
$ ,RSOILTMP)
IMPLICIT REAL (A-Z)
SAVE
IF( SOWN .EQ. 0. ) THEN
RSOILTMP = 0
ELSEIF(( SOWN .EQ. 1.).AND.((TIME-DOYSO) .GT. 0.)) THEN
RSOILTMP = SOILTMPI / DELT
ELSE
RSOILTMP = (DAVTMP - SOILTMP)* MSOIL
ENDIF
RETURN
END
18
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 4 8 12 16 20 24
Ph
oto
pe
rio
dic
fac
tor
Daylength (h d-1)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 20 40 60 80
Ve
rnal
izat
ion
fac
otr
Accumulated vernalized days (d)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
-10 -5 0 5 10 15 20 25
Ve
rnal
izat
ion
rat
e
Temperature (oC)
if
if
if
if
if
if
if
if
if ∫( )
∫( )
if
∫( )
if
Vsat
Vb
Tv2
2
Tv3
2
Tv1 Tv4
2
Popt
Pb
2
Figure 3.3 Vernalization and photosensitivity modules of winter wheat derived from Van Bussel et al. (2011)
19
CO2 effect on growth
In order to examine the effect of
changes in atmospheric CO2
concentrations, a model
component, derived from Supit et
al. (2012), that calculates the light
use efficiency (LUE) based on the
CO2 concentration in the air, was
added. The CO2 effect is inserted
by multiplying the LUE with a
correction factor. The value of this
correction factor is determined by
the CO2 concentration (Fig. 3.4).
The CO2 procedure in LINTUL can
be found in Box 3.3.
Reallocation
From the experimental data of Groot & Verberne (1991) it became clear that reallocation of
assimilates from the stem and leaves to the grains after anthesis is an important part of yield
development. Since this process was not directly taken up in the LINTUL model, this was added in the
present research. This reallocation module is source driven. This means that the rate of reallocation
from one organ to another (RA…) depends on the total amount of biomass in the source organ (W…).
In reality, reallocation might be more sink driven (Nátrová and Nátr, 1993). However, because
developing a complete source/sink driven model component would take too much time with respect
to the aim of this research, the simpler solution was chosen.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
0 500 1000 1500 2000 2500
Co
rre
ctio
n f
acto
r fo
r LU
E CO2 concentration (ppm)
Box 3.3: The CO2 procedure
GTOTAL = LUE * PARINT * CORLUE
CORLUE = AFGEN(TMPFTB,DTEMP) * AFGEN (COTB,CO2)
FUNCTION COTB = 40., 0.00, 360., 1.00, 720., 1.35,...
1000., 1.50, 2000., 1.50
Figure 3.4 The relation between the atmospheric CO2
concentration and the correction factor for LUE. The dotted line
represents the average CO2 concentration from 1981 until 2010.
20
The magnitude of RA… depends on W…, the weight of the source organ, and the fraction of the latter
which is reallocated daily (RF…). The size of the reallocation fractions depends on the DVS of the crop.
Reallocation only takes place from anthesis onwards. The reallocated assimilates from both stems
and leaves are added to the normal growth of the grains (Gtotal AFgrain) to get the rate of change of
grain weight (Rgrain). Similarly, these reallocation rates are subtracted from rates of change of stems,
leaves and LAI. In LINTUL the module is defined as shown in Box 3.4.
2.2.2 Calibration of the model
Development and growth of winter wheat was calibrated separately. For calibration, two datasets
were used. Firstly there was a dataset of Habekotte (1989) with development dates of winter wheat
from sowing to harvest from the 1970’s to 1990’s. Secondly an experimental data set from Groot &
Verberne (1991) with development, biomass and LAI data from spring until harvest was available.
2.2.2.1 Development
The development of winter wheat was calibrated with a data series of five different years and six
different sowing periods (Habekotte, 1989) (see Appendix II). To calibrate development of wheat for
a wide range of sowing times, observed data from winter wheat sown from the end of September to
January were used. Only observed data from years after 1971 was used, since before that no
weather data for LINTUL simulations was easily available. The years 1979, 1980 and 1982 until 1984
were selected to calibrate development, because the data for calibration of growth was collected in
the same period (1983-84). In the calibration procedure the standard deviation and the average of
the difference between actual and observed development dates and correlation between actual and
simulated stages were used to select optimal parameter values. The parameters were calibrated one
after another by moving step by step into the direction that gave the lowest standard deviation and
highest correlation between measured and simulated development and with an average difference
of about zero.
Emergence
The following parameters in LINTUL were calibrated for predicting the day of emergence of winter
wheat: base temperature for emergence, required temperature sum for emergence and the
conversion factor for changes in soil temperature based on air temperature. The initial value of base
temperature and temperature sum for emergence were based on Angus & Cunningham (1980)
(Tbasem = 2.6,Tsum-emergence = 78) and Bauer et al (1984) (Tbasem = 0,Tsum-emergence = 100). Ewert et al. (1996),
McMaster & Smika (1988) and Robertson (1968) reported periods of 11, 9 and 8.3 days for
emergence of wheat after sowing respectively. Using the parameters of Angus & Cunningham and
Box 3.4: The reallocation procedure
FUNCTION FLVRA = 0.0 ,0.00, 0.80,0.005, 1.00,0.010, ...
1.40,0.030, 1.60,0.030, 2.00,0.040
FUNCTION FSTRA = 0.0 ,0.00, 0.80,0.000, 1.00,0.000, ...
1.40,0.0075, 2.00,0.000
RWLVG = (GTOTAL * FLV - DLV - RALVSO)
RWST = (GTOTAL * FST - RASTSO)
RWSO = (GTOTAL * FSO + RASTSO + RALVSO)
RLAI = GLAI - DLAI - (RALVSO * SLA)
21
Bauer et al. comparable periods were found for the wheat crops described by Groot & Verberne
(1991). Therefore the average of these parameters was used as starting value for the emergence
module.
Anthesis
After calibration of emergence the development was calibrated for the time of anthesis. The
importance of vernalization and photosensitivity for development of winter wheat differs between
regions and varieties (Worland et al., 1994; McMaster and Smika, 1988). Therefore the performance
of different combinations of processes in predicting anthesis was evaluated based on standard
deviation and correlation between simulated and measured data. In this comparison the value for
Tbase in the thermal model and all the coefficients of the vernalization and photosensitivity models
were left undisturbed. Only the temperature sum was used to roughly calibrate the models after
which they were compared. The following combinations of processes were compared: Thermal,
Photo-Thermal, Vernal-Thermal and Photo-Vernal-Thermal.
The evaluation pointed out the full combination of photosensitivity, vernalization and a temperature
sum as the most accurately predicting model, which is in agreement with research on varieties from
surrounding countries like Germany and the UK (Davidson et al., 1985; Worland et al., 1994).
Therefore this combination was further calibrated to simulate time of anthesis.
Because in the latter evaluation the Photo-Thermal model performed better than the Vernal-Thermal
model, first the coefficients of the photosensitivity process were optimized in combination with the
base temperature and the temperature sum. After optimizing these two processes, the parameters
of the vernalization part of the model were calibrated. Again the base temperature and the
temperature sum were adapted if necessary to get an optimal combination with the vernalization
process.
Maturity
Since photosensitivity and vernalization are not involved in the development after anthesis (van
Bussel, 2011; Boote et al., 1996), the date of maturity is predicted based on a Tbase and a Tsum. In
LINTUL1, the Tbase for maturity is the same as for anthesis. Therefore, the final value for this
parameter was determined based on the highest accuracy for both processes. The Tsums for anthesis
and maturity were adapted to that.
2.2.2.2 Growth
For the calibration of growth of winter wheat in the Netherlands, experimental data from Groot &
Verberne (1991) was used. The experiments were carried out with wheat variety Arminda at three
locations on loamy soils in the Netherlands in the growing seasons 1982/83 and 1983/84. In the
experiments three nitrogen application levels were included: 0, 60, 120 kg N ha-1.
Since there was no water treatment included in the experiment it was not clear if the experiments
could be used for calibrating potential yield. Although it is not likely that drought stress occurred
because of the clayey soils, further evaluation was done to underpin the assumption that the crops
did not suffer from drought. For this evaluation the critical soil water content was calculated and
compared with the actual soil water content. The critical water content (θcr) can be calculated with
the potential transpiration rate of the crop (ETcrop) and, the crop transpiration factor (Tco) and water
content at field capacity (θfc) and wilting point (θwp).
22
( )
The water contents at field capacity and wilting point were derived from soil water retention curves
published by Groot & Verberne(1991).
The transpiration coefficient of winter wheat was derived from Van Oijen & Leffelaar (2008b)
The potential transpiration rate of the crop was calculated with reference crop evapotranspiration
(Er) and crop factor (f) derived from the KNMI. Since the reference crop evaporation is in fact a
reference evapotranspiration of both crop and soil (ET) (Hooghart, 1988), the potential transpiration
rate of the crop has to be separated from the potential soil evaporation. To do this the ratio
between the crop transpiration rate and total evapotranspiration is assumed to be equal to the ratio
between radiation intercepted (Iint) by the crop and total incoming radiation (I0). In that way the
potential crop evaporation rate is calculated with the following equation:
( )
Where k is the crop specific radiation extinction coefficient and L is the leaf area index of the crop.
Besides this the rooting depth was compared to the soil water table, to estimate water availability
due to capillary rise.
The investigation confirmed the assumption that no water stress occurred. The model was calibrated
for two years at the same time to make it more accurate for a range of years. This was done by
running the model for both growing seasons using similar parameters, so two runs for each
calibration step.
The starting values for the parameters of the different components involved in growth were
calculated from the data of Groot & Verberne (1991) For these calculations only experimental data
from winter wheat with the highest nitrogen application rate, as described in the article, was used.
Parameters that could not be directly calculated from the data of the experiments were derived from
literature or from previous versions of the model.
During the calibration process the initial organ weight values were adapted if necessary to create
optimal resemblance with the measured data. The simulated data was fit to the measured data. First
the initial LAI growth driven by temperature was calibrated, then assimilated based LAI growth and
allocation to different plant parts followed, and finally reallocation from vegetative to generative
plant tissue and death of leaves was adjusted. There were no boundaries set for the different
parameters. However, the parameter values were compared with the calculated values or literature
values if information on the parameters was available.
Since the model would be used for comparing two sowing dates, the sensitivity to varying sowing
dates had to be correct. However, the yield of simulated winter wheat dropped way too fast with
later sowing, compared to findings in literature (Habekotte, 1989). Therefore the allocation of
assimilates to leaves was spread more over the growing period. After recalibrating with the
experimental data, the error was nearly fixed.
23
2.2.3 Validation
The model was validated with experimental data of potential yields from different sources
(Darwinkel, 1994; Darwinkel, 1985); PPO Lelystad). For running the LINTUL1 model sowing dates and
initial plant weight or sowing density are required, therefore only experiments in which these dates
and sowing density or plant density were mentioned could be used. Only the data from treatments
with high nutrient applications, highest yields compared to other treatments in the same experiment
and no records of damaging events during growth were used in the first selection of calibration data.
From this selection yields from years with less than 200 mm cumulative precipitation from April to
July and yields which were clearly lower than other yields in the same year were removed to be sure
that they were not influenced by water stress (see Appendix III).
The initial weight of leaves and stems was calculated on the basis of sowing density (grain nr. or grain
weight per hectare) or plant density. To calculate plant density or sowing density the following
assumptions were made: 1000 grain weight was 40 gram (Ellen and Spiertz, 1980) and emergence
rate was 75% based on Hammink (2009) and Darwinkel (1994; Darwinkel).
2.2.4 Model for current varieties
After calibrating and validating the LINTUL model for the data of Groot & Verberne (1991), another
version of the model was made to represent winter wheat varieties around the year 2010. The
estimated grain yield of the model for varieties around 2010 was determined by combining the
average grain yield from the 120 kg N treatment from both growing seasons in the experimental
study by Groot & Verberne (1991), with the linear trend in genetic improvement of winter wheat
yields of 9.4 g DM m-2 year-1 as described by Rijk et al. (2013). The increase in grain production which
was necessary to reach the estimated grain production for the 2010 ‘varieties’, was realized by
raising the HI to 0.5 and increasing the LUE of the model. The choice of these parameters was based
on findings in literature that improvements in grain yields in winter wheat are mainly due to
increases in HI and LUE (Brancourt-Hulmel et al., 2003; Shearman et al., 2005; Foulkes et al., 2007).
The harvest index was increased by increasing the reallocation of assimilates from the stem to the
grains during the period of grain filling. First the average grain yield in the experiments was
calculated, then the expected yield of varieties from 2010 was computed based on the genetic
improvement over the period of 1983-84 to 2010. The model was calibrated with weather data from
the years 1981 to 1985 to the calculated yield for 2010. The model was run with weather data of the
same period as the experiments were conducted to exclude possible weather effects from the
simulations. The day of sowing in the calibration model was 296, which was the average of the
sowing dates in the experiments.
2.2.5 Model limitations
Since a model is always a simplification of the complexity of reality, it comes with its limitations. The
main limitation of this model in this case is the lack of a sink limitation with respect to the
reallocation of assimilates to the grains. The model is source driven which means that yield increases
due to assimilate production can be overestimated. Furthermore the model, like many other models
is quite static, meaning that in reality winter wheat plants might adapt more to variability within or
between seasons, this can cause deviations from reality if the input circumstances change a lot. For
instance the switching point between sink and source limited LAI growth depends on the LAI or the
development stage of the crop. These sink limitations are based on temperature limitations to cell
division. However the temperatures during the given development stages or LAI’s are very different
24
for early or late sown winter wheat, which makes the model less suitable for simulating different
sowing dates. Finally, the model includes vernalization and photosensitivity effects on development.
Analyses from Germany have shown that there are large variations in sensitivity for these processes
between different varieties. This makes it hard to calibrate development of different varieties over
30 year, based on only developmental data from before the 1990’s.
2.2.6 Simulation
To investigate different effects on winter wheat yield trends, 16 different simulations runs were done
in total. These 16 runs resulted from: 2 model versions for different ‘varieties’ x 2 sowing dates x 4
weather factor runs.
2.2.6.1 Sowing date
Since Timmer (2012) and Veeman (2012) indicated that the sowing date of wheat changed during the
period of 1981 to 2012, the models were run for the ideal simulation date (15 October) and a late
sowing date (25 November).
2.2.6.2 Different varieties
There could be interactions between the change in physiological properties of wheat varieties due to
breeding and changes in weather factors. To investigate these possible interactions two input sets for
the LINTUL model were used for simulations. The first set was calibrated for winter wheat varieties
used in the early 1980’s. Based on this set a second set was adapted for varieties in the late 2000’s
(see 2.2.4).
2.2.6.3 Weather factors
To distinguish between the effect of different weather factors on winter wheat yields in the
Netherlands from 1981 to 2010, the simulations were done with four different weather files. For
each factor, viz. daily minimum and maximum temperature, daily total incoming global radiation and
average yearly CO2 concentration in the air, a separate run was done in which the other factors were
averaged for the same day over the period 1981 to 2010. For example, to see the effect of solely
temperature on winter wheat yields, the daily total incoming radiation was averaged for each day of
the year over the period of 1981 to 2010. These averages were inserted in all weather files from 1981
to 2010 together with the actual temperature data for these years. Because the CO2 concentration
was included in the model on a yearly basis and not on daily basis, the average for this factor was
included as one yearly average over the investigated period.
To investigate the effect of solely CO2 concentration changes, only two weather files with averages
were calculated, one with 365 days for normal years and one with 366 days for leap years. The CO2
concentrations for the 30 different years were directly included in the model and not in the weather
files. During simulations the normal weather file was used for normal years and the leap year
weather file was used for leap years.
After the three runs for separate factor analysis, a final run was done with all actual daily
(temperature, radiation) or yearly (CO2) values for the different factors. In this way the combined
effect of the different weather factors on winter wheat yields from 1981 to 2010 was investigated.
For some days certain values of temperature or radiation data was missing due to measuring errors.
These missing values were replaced by the average value for that day over the other 29 years.
26
3 Results
3.1 Area and yield analyses
3.1.1 Area
Over the investigated period the total yearly winter wheat acreage showed no significant changes.
There were however changes in regional acreage of winter wheat (App. I: Fig. I.1, I.2). The northern
areas ‘Bouwhoek & Hogeland’ and ‘Veenkoloniën & Oldambt’ did not significantly change over the
years (App. I: Fig. I.1a). The regions ‘Zuidwestelijk akkerbouwgebied’, ‘ IJsselmeerpolders’ and
‘Westelijk Holland’ had quadratic trends, with declining acreages in the beginning of the period of
investigation which turn into an increase when approaching the year 2010 (App. I: Figs I.1a,b). An
inverse trend was found for ‘Zuid-Limburg’, where the quadratic trends increased in the first fifteen
years and shrunk again in the last five years (App. I: Fig. I.1c). In some areas there was a significant
linear increase in winter wheat acreage in the period of 1981 to 2010, namely: ‘Rivierengebied’,
‘Zuid-West Brabant’, ‘Zuidelijk veehouderijgebied’, ‘Noordelijk weidegebied’, ‘Centraal
veehouderijgebied’ and ‘Oostelijk veehouderijgebied’ (App. I: Figs I.1b,c I.2a,b). Acreages in the
‘Hollands-Utrechts weidegebied’ continuously increased, with low rates of growth at the beginning of
the period and 1981 and high rates at the end of the period (Fig. I.2a). In the region ‘Waterland &
Droogmakerijen’ the average area cropped with winter wheat decreased linearly (App. I: Fig. I.2a).
3.1.2 Yields
The National winter wheat yields increased linearly (P < 0.05) with 66.3 kg ha-1 annually from 1981
until 2010 (Fig. 3.1). In the period of 1977 or earlier up to 2010 yield increases were quadratic, with a
decline in growth later in the period (Fig. 3.2). This quadratic trend was also found for periods
starting earlier in the 70’s (dotted line) and going up to 2010. With data from before 1981 included,
still there was no significant trend in the national yearly area of winter wheat. Regional yield trends
were comparable to the national trend (App I.: Fig. I.3), with linear increases of 64 to 91 kg ha-1
year-1.
y = 7.239 + 0.0663x R² = 0.52
0
50
100
150
200
250
300
0
2
4
6
8
10
12
1980 1985 1990 1995 2000 2005 2010 2015
Are
a (1
00
0 h
a)
Pro
du
ctiv
ity
(to
n h
a-1)
Year
Productivity
Area
*
Figure 3.1 Average national yields and area of winter wheat in the Netherlands from 1981 until 2010. Data
series marked with an asterisk do not show a significant trend (P < 0.05). In equations x = year - 1981.
27
Figure 3.2 Average National yields and area of winter wheat in the Netherlands from 1970 until 2010. Data
series marked with an asterisk do not show a significant trend (P < 0.05). In equations x = year - 1981.
y = 6.73 + 0.15x - 0.00285x2 R² = 0.86
0
50
100
150
200
250
300
0
2
4
6
8
10
12
1965 1975 1985 1995 2005 2015
Are
a (1
00
0 h
a)
Pro
du
ctiv
ity
(to
n h
a-1)
Year
Productivity
Area
*
28
3.2 Weather and CO2 trends
3.2.1 CO2
The annual average CO2 concentration in the air increased quadratically (Fchange < 0.001) from 340
PPM in 1981 to 390 PPM in 2010.
3.2.2 Weather
3.2.2.1 Average daily temperature
The mean daily temperature averaged over the periods of mid-October to mid-July, mid-April to mid-
July and mid-June to mid-July increased linearly (F ≤ 0.024) from 1981 to 2010 for all weather
stations, with 0.047 to 0.053, 0.056 to 0.066 and 0.055 to 0.060 oC year-1, respectively (Figs 3.4 &
App. I: Fig. I.4).
y = 340 + 1.26x + 0.0148x2 R² = 0.998
320
340
360
380
400
1980 1985 1990 1995 2000 2005 2010 2015
CO
2 co
nce
ntr
atio
n a
ir (
µm
ol m
ol-1
)
Year
y = 15.97 + 0.0446x R² = 0.11
y = 12.99 + 0.0560x R² = 0.37
y = 8.19 + 0.0467 R² = 0.20
5.00
7.00
9.00
11.00
13.00
15.00
17.00
19.00
1980 1985 1990 1995 2000 2005 2010 2015
Ave
rage
tem
per
atu
re (
oC
)
Year
Figure 3.3 Trend in CO2 concentrations in air from 1981 to 2010. In the equation x = year - 1981.
Figure 3.4 Trends in mean daily temperature averaged over a period of June to July (green), April to July
(blue) and October to July (red) from 1981 to 2010 at De Bilt, Netherlands. In the equations x = year - 1981.
0.00
29
3.2.2.2 Cumulative incoming global radiation
The incoming global radiation accumulated from mid-October to mid-July and mid-April to mid-July
increased linearly (F ≤ 0.008) over the investigated period at all weather stations (Fig. 3.5 & App. I:
Fig. I.5). At Twenthe the increase was 14.4 and 15.5 MJ m-2 year-1 from 1987 to 2010, respectively.
For the other stations it was 7.94 to 8.77 and 7.28 to 9.65 MJ m-2 year-1 from 1981 to 2010,
respectively.
3.2.2.3 Cumulative reference and actual evapotranspiration
Both the daily reference and actual evapotranspiration accumulated from mid-April to mid-July
increased linearly (F ≤ 0.003) at all weather stations over the investigated period (Fig. 3.6 & App. I:
Fig. I.6). At weather station Twenthe the reference and actual evapotranspiration increased with 2.73
and 2.42 mm year-1, respectively. At the other stations the increases were 1.74 to 1.83 and 1.59 to
1.67 mm year-1, respectively.
y = 284 + 1.62 R² = 0.25
y = 302.8 + 1.77x R² = 0.27
200
250
300
350
400
1980 1985 1990 1995 2000 2005 2010 2015
Cu
mu
lati
ve e
vap
otr
ansp
irat
ion
(m
m)
Year
y = 1880 + 8.57x R² = 0.22
y = 1639 + 9.65x R² = 0.21
1500
2000
2500
3000
3500
4000
1980 1985 1990 1995 2000 2005 2010 2015
Glo
bal
rad
iati
on
(M
J m
-2)
Year Figure 3.5 Trends in incoming global radiation accumulated over a period of April to July (blue) and October
to July (red) from 1981 to 2010 at De Bilt, Netherlands. In the equations x = = year - 1981.
Figure 3.6 Trends in reference (blue) and actual (green) evapotranspiration of winter wheat (Triticum
aestivum L.) accumulated over a period of April to July from 1981 to 2010 at De Bilt, Netherlands. In the
equations x = = year - 1981.
0
0
30
3.2.2.4 Rainfall and precipitation deficit
Daily rainfall accumulated from mid-April to mid-July and the precipitation accumulated from mid-
April to mid-July and from mid-June to mid-July did not show a significant (P < 0.05) trend for all
weather stations (Fig 3.7 & App. I: Fig. I.6).
3.3 Correlation between winter wheat yields and weather factors and CO2
3.3.1 Correlations between factors
Very strong correlations (>80%) were found between global radiation and evapotranspiration and
between rainfall and precipitation deficit (Table 3.1 & App. I: Tables I.1-I.3). Precipitation deficit,
rainfall and average temperatures in spring and summer correlated strongly (45-80%) with global
radiation and evapotranspiration, as well average temperatures from mid-April to mid-July with
precipitation deficit.
3.3.2 Models with combined factors
Average temperature during the growing season was included in seven of the ten models with a
negative effect on yields, precipitation deficit from June to July was included in two models with a
positive effect, besides those precipitation deficit from April to July with positive effect, rainfall from
April to July with negative effects and CO2 with positive effects, were selected (Table 3.2). The
regions Oostelijk veehouderijgebied, Centraal veehouderijgebied, Westelijk Holland, Waterland &
Droogmakerijen, Rivierengebied and Zuidelijk veehouderijgebied were only influenced by weather
via the average temperature during the growing season. The regions IJsselmeerpolders,
Hollands/Utrechts weidegebied and Zuidwestelijk akkerbouwgebied were influenced by,
respectively, precipitation deficit from April to July, rainfall from April to July and precipitation deficit
from June to July. Climate effects on Zuid-Limburg included average temperature during the growing
season, precipitation deficit from April to July and CO2. The regression model for Zuid-Limburg has
unexpected coefficient values. This might be due to aliasing between CO2 concentrations in the air
and years.
-200
-100
0
100
200
300
400
500
1980 1985 1990 1995 2000 2005 2010 2015
Pre
cip
itat
ion
def
icit
& R
ain
fall
(mm
)
Year
Figure 3.7 Trends in rainfall and precipitation deficit of winter wheat (Triticum aestivum L.) accumulated
over a period of April to July (rainfall [red], precipitation deficit [green]) and June - July (precipitation deficit
[blue]) from 1981 to 2010 at De Bilt, Netherlands. In the equations x = year -1981.
31
Table 3.1 Correlations between weather factors and CO2 for weather station De Bilt, Netherlands over de period 1981 - 2010.
Actual evapotranspiration (April - July) (mm) 1.00
Reference evapotranspiration (October - July) (mm) 1.00 1.00
Global radiation (April - July) (MJ m-2) 0.98 0.99 1.00
Global radiation (October - July) (MJ m-2) 0.91 0.92 0.94 1.00
Precipitation deficit (April - July) (mm) 0.70 0.70 0.75 0.74 1.00
Precipitation deficit (June - July) (mm) 0.62 0.62 0.63 0.61 0.85 1.00
Rainfall (April - July) (mm) -0.46 -0.46 -0.53 -0.55 -0.96 -0.80 1.00
Average temperature (October - July) oC 0.36 0.37 0.29 0.21 -0.12 -0.12 0.29 1.00
Average temperature (April - July) oC 0.69 0.70 0.58 0.50 0.13 0.19 0.11 0.71 1.00
Average temperature (June - July) oC 0.76 0.76 0.69 0.64 0.46 0.65 -0.26 0.33 0.72 1.00
Year 0.52 0.54 0.50 0.49 0.09 0.03 0.10 0.48 0.62 0.38 1.00
CO2 ppm 0.54 0.56 0.52 0.51 0.11 0.04 0.08 0.48 0.63 0.39 1.00 1.00
(mm
)
(mm
)
(MJ m
-2)
(MJ m
-2)
(mm
)
(mm
)
(mm
)
oC
oC
oC
pp
m
Actu
al
evapo
transp
iration
(A
pril - Ju
ly)
Refe
rence
evapo
transp
iration
(Octo
ber - Ju
ly)
Glo
bal rad
iation
(A
pril - Ju
ly)
Glo
bal rad
iation
(O
ctob
er - July)
Precip
itation
deficit
(Ap
ril - July)
Precip
itation
deficit
(Jun
e - July)
Rain
fall (Ap
ril - Ju
ly)
Ave
rage te
mp
erature
(Octo
ber - Ju
ly)
Ave
rage
tem
peratu
re (Ap
ril - Ju
ly)
Ave
rage
tem
peratu
re (Jun
e - Ju
ly)
Year
CO
2
Legend
|Correlation| -
1.0 - 0.8
0.7 - 0.8
0.5 - 0.7
0.4 - 0.5
0.2 - 0.4
0 - 0.2
32
Table 3.2 Coefficients of multivariate linear regression analyses on relation between climate factors an winter wheat yields at 15% moisture (kg ha-1
) in the Netherlands
from 1981 - 2010 using bidirectional elimination.
Region
Model P
R2adj.
Constant
Year
Average
temperature
GSa (oC)
Precipitation
deficit
JJ b (mm)
Precipitation
deficit
AJ c (mm)
Rainfall
AJ c (mm)
CO2
(PPM)
Oostelijk veehouderijgebied 9368 98.0 -414 <.001 0.61
Centraal veehouderijgebied 9665 88.3 -487 <.001 0.53
IJsselmeerpolders 7262 71.45 4.87 <.001 0.65
Westelijk Holland 13109 -499 0.007 0.32
Waterland & Droogmakerijen 12941 -460 0.053 0.16 Hollands/Utrechts
weidegebied 9964 -8.14 0.006 0.34
Rivierengebied 10289 93.1 -419 <.001 0.67 Zuidwestelijk
akkerbouwgebied 7448 62.2 4.29 <.001 0.57
Zuidelijk veehouderijgebied 8805 79.7 -312 <.001 0.59
Zuid-Limburg 71128 411 -313 2.75 182.9 <.001 0.78
a Growing season: October - July
b June - July
c April - July
33
3.4 Calibration
3.4.1 Development
3.4.1.1 Emergence
Table 3.3 shows the model parameters before and after calibration. The adjusted relations in the
model can be found in figure 3.8 and App. I: figs I.8 and I.9.
Observed and simulated dates of emergence, start of grain filling and maturity can be found in figure
3.9. Since these dates are expressed in Julian day of the growing season, there is a difference of 365
days between crops sown before and after New Year.
Table 3.3 Original (spring wheat) and calibrated (winter wheat) values of parameters for the LINTUL winter
wheat (Triticum aestivum L.) model in the Netherlands.
Parameter Parameter in Model
Unit Original
Calibrated
Development
Tsum-emergence TSUMEM
oC day 89b
122
Tbasem TBASEM
oC 1.3b
0.25
Tb TBASE
oC 0
1.5
Tsum-anthesis TSUMAN
oC day 720
926
Tsum-maturity TSUMMT
oC day 950
590
Vsat VERSAT
Day 70
58
M MSOIL
- 0.25a
0.25
Leaf area
Tsum-ageing TSUMAG
oC day 720
900
Sla-correction SLAC
- 0.0212
0.021
rl RGRL
oC day-1 0.00817
0.015
Lcr LAICR
- 4
4
Rd-shmx RDRSHM
day-1 0.03
0.03
Maximum sink limited LAI - 0.75 0.6
Assimilate production
Wlvgi WLVGI
g m-1 0.16c
0.07
Wsti WSTI
g m-1 0.08c
0.04
LUE LUE
g MJ-1 3.00
3.15
k K
- 0.6
0.6
a Source: (Zheng et al., 1993)
b Based on an average of Angus (1980) and Bauer (1984)
c Based on data from Boons - Prins et al. (1993) and Van Heemst (1988)
34
Figure 3.9 Simulated and observed dates of emergence, start of grain filling and maturity of
winter wheat in the Netherlands in Julian day where January 1st
= 1.
0
0.2
0.4
0.6
0.8
1
1.2
0 0.5 1 1.5 2
Allo
cati
on
fac
tor
(d-1
)
Development stage
Stem - calibrated
Stem - original
Grain - calibrated
Grain - original
Roots - calibrated
Roots - original
Leaves -calibrated
Leaves - original
Figure 3.8 Original and calibrated relation between development stage and allocations factors of assimilates
to the roots, stem, leaves and grain (d-1
) of winter wheat (Triticum aestivum L.).
-150
-100
-50
0
50
100
150
200
250
300
-150 -100 -50 0 50 100 150 200 250 300
Sim
ula
ted
de
velo
pm
en
t (j
ulia
n d
ay)
Observed development (julian day)
Maturity
Start grainfillingEmergence
1:1 line
35
3.4.2 Growth
3.4.2.1 Initial plant weight
The initial weight of stem and leaves were derived from data of Boons - Prins et al. (1993) and Van
Heemst (1988). The average plant rate is 45 plants m-2 and the initial plant weight is 0.011 g plant-1
both given by Van Heemst (1988). According to Boons - Prins et al. (1993) half of the plant weight is
aboveground biomass and 65% of the aboveground biomass is in the leaves.
Initial total dry weight = 0.011 * 45 = 4.95 g m-2
Initial total aboveground biomass = 4.95 * 0.5 = 0.25 g m-2
Initial weight green leaves = 0.2475 * 0.65 = 0.16 g m-2
Initial weight stem = 0.2475 * 0.65 = 0.087 g m-2
3.4.2.2 Evaluation of calibration data
The average water content of the rooted zone only slightly went under the critical water holding
content for The Bouwing in both years (App. I: Figs I.10 I.11). For all locations the depth between the
root zone and the groundwater table never exceeded one meter after the 1st of May (App. I: Fig I.12).
36
3.4.2.3 Calibration growth
Figure 3.10 Simulated (lines) and measured (symbols) aboveground biomass [a], leaf area index [b], weight of green leaves [c], weight of death leaves [d], stem + chaff
[e], grain yields of winter wheat [f] in 1983 (red) and 1984 (green) at The Bouwing (□), The Eest (∆) and PAGV (○).
0
200
400
600
800
1000
1200
1400
1600
1800
2000
350 450 550
Ab
ove
gro
un
d b
iom
ass
(kg
ha
-1)
Julian day
0
1
2
3
4
5
6
350 450 550
LAI
Julian day
0
50
100
150
200
250
300
350 450 550
Gre
en
leav
es
(kg
ha
-1)
Julian day
0
20
40
60
80
100
120
140
160
180
200
350 450 550
De
ad le
ave
s (k
g h
a-1
)
Julian day
0
200
400
600
800
1000
1200
350 450 550
Ste
m w
eig
ht
(kg
ha
-1)
Julian day
0
100
200
300
400
500
600
700
800
900
350 450 550
Gra
in y
ield
(kg
ha
-1)
Julian day
a b c
d e f
37
There was a correlation of 30% between observed and simulated yields (Fig. 3.11). Crops with high or
low plant densities were all underestimated by the LINTUL model. There seemed to be a tendency to
overestimate low yields and underestimate high yields.
Figure 3.11 Simulated and observed yields (15% moisture) of winter wheat in
the Netherlands sown in September (yellow), October (green) and November
or later (purple), before (closed symbols) and after (open symbols) 1990,
grown at plant densities lower than 250 plants m-2
(◊), between 250 and 400
plant m-2
(○) and higher than 400 plants m-2
(□). Sources: Darwinkel, 1994;
Darwinkel, 1985; PPO Lelystad, 2012. Additional information in App. III.
R² = 0.30
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
5.0 7.0 9.0 11.0
Sim
ula
ted
yie
ld (
g m
-2)
Observed yield (g m-2)
38
3.5 Simulations
3.5.1 Influence of temperature change
Time of emergence (Fig. 3.12a) and duration of the period from anthesis to harvest (App. I: Fig. I.13a)
of normal sown winter wheat did not show a significant (F > 0.05) trend over time. Time of anthesis
and harvest (Fig. 3.12a) both declined linearly (P < 0.05), with 0.37 and 0.48 days year-1 or 11 and 14
days in 30 years, respectively. The duration of the period between emergence and harvest also
declined, with 0.35 days year-1, i.e. 10 days in 30 years (App. I: Fig. I.13a). Time of emergence and
anthesis (Fig. 3.12b) and duration of the periods from emergence to anthesis and the period from
anthesis to harvest (App. I: Fig. I.13a) of late sown winter wheat did not show a significant (P < 0.05)
trend over time. The time of harvest did decline with 0.41 days year-1 or 12 days in 30 years (App. I:
Fig. I.13a). The deviation between the longest day (21 June) and the median day of the period from
anthesis to harvest decreased linearly (P < 0.05) from 1981 to 2010 for both sowing dates (App. I: Fig.
I.13b). The magnitudes of decrease were 0.34 and 0.41 days per year i.e. 10 and 12 days in 30 years.
The cumulative incoming global radiation during the period from emergence to anthesis decreased
linearly (P < 0.05) with 6.63 and 7.06 KJ m-2 year-1 for, respectively, normal and late sown winter
wheat from 1981 to 2010 (Fig. 3.12c). For the period after anthesis cumulative incoming global
radiation did not show a significant trend for both sowing dates (Fig. 3.12c). The average value of the
LUE correction factor for mean daily temperature only showed a significant (P < 0.05) trend for the
period of emergence to anthesis of normal sown winter wheat (App. I: Fig. I.13b). No significant
change was found for the period after anthesis of late sown winter wheat and for both periods of the
normal sown crop.
For both ‘varieties’ and sowing dates of winter wheat no significant (P < 0.05) trend in simulated total
aboveground biomass at anthesis and harvest, grain yields and harvest index, were found over the
period 1981-2010 (App. I: Figs. I.14 & I.15).
3.5.2 Influence of radiation change
Total incoming global radiation before anthesis increased linearly (P < 0.05) with 12.3 and 11.9 kJ m-2
per year from 1981 to 2010, for normal and late sown winter wheat respectively (Fig. 3.14a). No
significant trend was found for total incoming global radiation after anthesis (Fig. 3.14a)
The grain yield and total aboveground biomass at anthesis and harvest increased linearly from 1981
to 2010 for both varieties and sowing dates (Fig. 3.14b, App. I: Table I.5) No significant (P < 0.05)
trend was found for the harvest index of winter wheat for all varieties and sowing dates (Fig. 3.14c,
App. I: Table I.5)
0
39
∆y = 4.27*10-3x R2 = 0.19
0.40
0.60
0.80
1.00
1.20
1980 1985 1990 1995 2000 2005 2010 2015
Co
rre
ctio
n f
acto
r o
f L
UE
fo
r d
ay
tem
pe
ratu
re
Year
Normal sown - emergence until anthesisLate sown - emergence until anthesis
*
a
c
R² = 0.39; ∆y = -0.483x
R² = 0.30; ∆y = -0.374x
250
300
350
400
450
500
550
600
650
1980 1985 1990 1995 2000 2005 2010 2015
Tim
e (
day
of
year
+ 3
65
)
Year
Harvest Anthesis Emergence
R² = 0.27; ∆y = 6.63x
R² = 0.32; ∆y = 7.06x
0
500
1000
1500
2000
2500
1980 1985 1990 1995 2000 2005 2010 2015C
um
mu
lati
ve r
adia
tio
n (
kJ m
-2 )
Year
Normal sown - anthesis to harvest Late sowing - anthesis to harvest
Normal sown - emergence to anthesis Late sown - emergence to anthesis
*
R² = 0.15; ∆y = -0.407x
250
300
350
400
450
500
550
600
650
1980 1985 1990 1995 2000 2005 2010 2015
Tim
e (
day
of
year
+ 3
65
)
Year
Harvest Anthesis Emergence
*
*
a
*
*
a
Figure 3.12 Simulated time of emergence, anthesis and harvest normal (10 October) [a] and late (25 November) [b] sown winter wheat, based on average data for radiation and annual average CO2
levels over the period of 1981 to 2010 and actual
minimum and maximum temperatures, using the LINTUL model. In the equations of the relationships x is in years with x = 0 year - 1981. ∆ stands for the slope of a trend. Data series marked with an * do not show a significant (P < 0.05) trend. Additional information can be found in Appendix I, Table I.4
c b
0
0
0
0
Figure 3.13 Simulated average correction factor for LUE [a] and cumulative radiation [b] during simulated crop stages of normal (10 October) and late (25 November) sown winter wheat, based on average data for radiation and annual average CO2
levels over
the period of 1981 to 2010 and actual minimum and maximum temperatures, using the LINTUL model. In the equations of the relationships x is in years with x = year - 1981. ∆ stands for the slope of a trend. Data series marked with an * do not show a significant (P < 0.05) trend. Additional information can be found in Appendix I, Table I.4
40
R² = 0.44 ∆y = 0.151x
R² = 0.46 ∆y = 0.129x
R² = 0.21 ∆y = 0.0659x
5
10
15
20
25
30
1980 1985 1990 1995 2000 2005 2010 2015
Ab
ove
gro
un
d b
iom
ass
(Mg
ha
-2)
Year
Aboveground biomass - Harvest Aboveground biomass - Anthesis Grain
R² = 0.49 ∆y = 12.3x
R² = 0.48; ∆y = 11.9x
0
500
1000
1500
2000
2500
3000
1980 1985 1990 1995 2000 2005 2010 2015
Cu
mm
ula
tive
rad
iati
on
(M
J m
-2 )
Year
Normal sown - anthesis to harvest Late sown - anthesis to harvestNormal sown - emergence to anthesis Late sown - emergence to anthesis
*
0.35
0.40
0.45
0.50
0.55
1980 1985 1990 1995 2000 2005 2010 2015
Har
vest
In
de
x
Year
Figure 3.14 Cumulative radiation during simulated crop stages of normal (10 October) and Late (25
November) sown winter wheat [a] simulated aboveground biomass (DM) at anthesis and harvest and grain
yield (15% moisture) [b] and harvest index [c] of a normal sown (10 October) winter wheat crop in the
Netherlands, based on daily temperatures and annual CO2 concentrations averaged over the period 1980 -
2010 and actual daily radiation levels using the LINTUL1 model calibrated for the early 1980’s . Equations of
the relationships are described in table I.5. ∆ stands for the slope of a trend. Data series marked with an *
do not show a significant (P < 0.05) trend.
*
0
0
a
b
c
0
41
R² = 1.0 ∆y = 0.134x - 2.07*10-3x2
∆y = 0.100x - 1.55*10-3x2
R² = 1.0
∆y = 0.0621x - 9.62*10-4 x2
R² = 1.0
5
10
15
20
25
1980 1985 1990 1995 2000 2005 2010 2015
Ab
ove
gro
un
d b
iom
ass
(Mg
ha
-2)
Year
Aboveground biomass - Harvest aboveground biomass - Anthesis Grain
R² = 1.0 ∆y = 5.5*10-3x - 8.55*10-5x2
0.90
0.95
1.00
1.05
1.10
1980 1985 1990 1995 2000 2005 2010 2015
CO
2 co
rre
ctio
n f
acto
r fo
r LU
E
Year
R2 = 1.0 ∆y = 2.97*10-4x - 4.82*10-6x2
0.35
0.40
0.45
0.50
0.55
1980 1985 1990 1995 2000 2005 2010 2015
Har
vest
In
de
x
Year
Figure 3.15 Simulated value of correction factor for the LUE of winter wheat crops in the Netherlands,
based on CO2 concentrations in the air [a] simulated aboveground biomass (DM) at anthesis and harvest
and grain yield (15% moisture) [b] and harvest index [c] of a normal (10 October) sown winter wheat crop in
the Netherlands, based on average weather data from 1980 to 2010 and actual CO2 levels in the air using
the LINTUL1 model calibrated for the early 1980’s In the equations of the relationships x is in years with x =
0 for the year 1981. ∆ stands for the slope of a trend. Data series marked with an * do not show a
significant (F < 0.05) trend. Additional information can be found in table I.6
a
b
c
0
0
0
42
3.5.3 Influence of CO2 change
The average LUE correction factor for CO2 showed a quadratic trend (P < 0.05) with a diminishing
increase from 1981 to 2010 (Fig. 3.15a). As a result the grain yield and total aboveground biomass at
anthesis and harvest showed a similar trend over the period 1981 - 2010 for both varieties and
sowing dates (Fig. 3.15b, App. I: Table I.6). The harvest index showed an opposing quadratic trend
over that period, the trend was a diminishing decline (3.15c, App. I: Table I.6)
3.5.4 Influence of actual weather
Time of emergence (Fig. 3.12a) and duration of the period from anthesis to harvest (App. I: Fig.
I.13a) of normal sown winter wheat did not show a significant (P < 0.05) trend over time. Time of
anthesis and harvest (Fig. 3.12a,b) of both declined linearly (P < 0.05), with 0.37 and 0.48 days year-1
or 11 and 14 days in 30 years, respectively. The duration of the period between emergence and
harvest also declined, with 0.35 days year-1 i.e. 10 days in 30 years (App. I: Fig. I.13a). Time of
emergence and anthesis (Fig. 3.12b) and duration of the periods from emergence to anthesis and
from anthesis to harvest (App. I: Fig. I.13a) of normal sown winter wheat did not show a significant (P
< 0.05) trend over time. The time of harvest did advance earlier with 0.41 days year-1 or 12 days in 30
years
For late sown winter wheat a significant (P < 0.05) positive quadratic trend in total incoming global
radiation was found for the period between anthesis and harvest (App. I: Fig. I.16a). Total incoming
global radiation did not change significantly after anthesis for normal sown winter wheat and before
anthesis for both sowing dates (App. I: Fig. I.16b).
The aboveground biomass at anthesis and harvest shows a significant linear trend for normal sown
and quadratic trend for late sown winter wheat (Fig. 3.16a,b). The difference between normal and
late sown winter wheat is explained by the effect of the LUE correction factor due to temperature
change (Fig. 3.13a). The grain yield of both models and sowing dates shows a quadratic trend (Fig.
3.17a). This trend can be explained by the quadratic effect of the CO2 concentrations on the LUE
combined with the quadratic effect on total biomass of late sown winter wheat. The harvest index
decreases for both varieties and sowing dates with 0.03 to 0.05 over 30 years (Fig. 3.17b).
43
R² = 0.52; ∆y = 0.163x
R² = 0.58 ∆y = 0.211x
R² = 0.52 ∆y = 0.171x
R² = 0.58; ∆y = 0.222x
5
10
15
20
25
30
1980 1985 1990 1995 2000 2005 2010 2015
Ab
ove
gro
un
d b
iom
ass
(Mg
DM
ha
-2)
Year
Anthesis - V1 Harvest - V1Anthesis - V2 Harvest - V2
R² = 0.13 ∆y = -9.98*10-4x
R² = 0.15
∆y = -1.32*10-3x
R² = 0.17 ∆y = -1.13*10-3x
∆y = -1.50*10-3x R² = 0.20
0.35
0.40
0.45
0.50
0.55
0.60
0.65
1980 1985 1990 1995 2000 2005 2010 2015H
arve
st I
nd
ex
Year
Normal sown - V1 Late sown - V1
Normal sown - V2 Late sown - V2
∆y = 0.467x - 0.0102x2
R² = 0.50
R² = 0.56 ∆y = 0.583x - 0.123x2
∆y = 0.492x - 0.0107x2 R² = 0.50
∆y = 0.605x - 0.127x2
R² = 0.56
5
10
15
20
25
30
1980 1985 1990 1995 2000 2005 2010 2015
Ab
ove
gro
un
d b
iom
ass
(Mg
ha
-2)
Year
Anthesis - V1 Harvest - V1Anthesis - V2 Harvest - V2
∆y = 0.204x - 0.00412x2
R² = 0.61
R² = 0.63; ∆y = 0.278x - 0.006063 x2
R² = 0.64; ∆y = 0.250x - 0.00508x2
R² = 0.64 ∆y = 0.324x - 0.00703x2
6
8
10
12
14
16
1980 1990 2000 2010
Gra
in 1
5%
mo
istu
re (
Mg
ha
-2)
Year
Normal sowing - V1 Late sowing - V1
Normal sowing - V2 Late sowing - V2
3.16 Simulated aboveground biomass at anthesis and harvest of normal (10 October) [a] and late (25 November) [b] sown winter wheat, based on real weather data using the LINTUL model calibrated for the early 1980’s (V1) and a model adjusted to varieties around 2010 (V2). In the equations of the relationships x is in years with x = year - 1989. ∆ stands for the slope of a trend. Data series marked with an * do not show a significant (P < 0.05) trend. Additional information can be found in table I.7
a
0
0
0
0.00
a
b
a
b
3.17 Simulated grain yield (15% moisture) [a] and harvest index [b] of normal (10 October and late (25 November) sown winter wheat, based on real weather data using the LINTUL model calibrated for the early 1980’s (V1) and a model adjusted to varieties around 2010 (V2). In the equations of the relationships x is in years with x = year - 1989. ∆ stands for the slope of a trend. Data series marked with an * do not show a significant (P < 0.05) trend. Additional information can be found in table I.7
44
4 Discussion
4.1 Yield The yield trend analysis showed that there was no stagnation in growth of winter wheat yields from
1981 to 2010 in the Netherlands both at national and regional level. This was also found by Rijk
(2013). The analyses of national yield trends starting between 1970 and 1977 up to 2010, however,
showed a quadratic trend, suggesting a levelling off of the yields indeed. This would be in accordance
with findings of Brisson et al. (2010) and Petersen et. al. (2010) who found that yields in the
Netherlands stagnated after 1993. However, it is important to notice that Brisson et al. (2010) and
Petersen et. al. (2010) used data from the FAO instead of CBS and that they investigated yields over a
period of ± 1960 to 2007. Also for most other countries in Europe stagnation of yield growth was
found or suggested, namely France (Brisson et al., 2010; Calderini and Slafer, 1998; Petersen et al.,
2010), United Kingdom (Knight et al., 2012; Brisson et al., 2010; Calderini and Slafer, 1998; Petersen
et al., 2010), Denmark (Slafer and Peltonen-Sainio, 2001; Brisson et al., 2010; Petersen et al., 2010),
Switzerland (Finger, 2010; Brisson et al., 2010), Finland (Slafer and Peltonen-Sainio, 2001; Peltonen-
Sainio et al., 2009), Sweden (Batts et al., 1996; Petersen et al., 2010), Belgium (Petersen et al., 2010)
and Norway (Slafer and Peltonen-Sainio, 2001). The only North-Western European country which is
not experiencing yield stagnation in winter wheat seems to be Germany (Brisson et al., 2010;
Calderini and Slafer, 1998), although Petersen (2010) suggests there is has been no yield increase
after 1998. Also on world scale there are indications of stagnation of winter wheat yields. Already in
1998 Calderini & Slafer found signs for this in 65% of the wheat growing areas.
45
4.2 Area The national cultivated area of winter wheat did not change, even though some regional cropped
areas did change. Also for other countries in Europe the area of winter wheat did not change for this
period. Petersen et al (2010) found that the total area of wheat cultivation has been more or less
stable since the 1970’s for Belgium, Germany, Netherlands, Sweden and France (also found by
Brisson et al (2010)). Both Petersen et al (2010) and Calderini & Slafer (1998) found that the cropped
area in UK was still rising in the beginning of the 1980’s but did not change after 1990.
Figure 5.1 Soil map of the Netherlands with agricultural regions according to CBS after 1990.
46
The changes in regional areas cropped with winter wheat show that in a lot of sandy regions area
increased, while in regions with clayey or loamy soils the areas decreased (Fig. 5.1 and App. I.1,2).
Yields in the sandy regions Oostelijk veehouderijgebied, Centraal veehouderijgebied and Zuidelijk
veehouderijgebied are on average 18% lower than the national yield per ha. Therefore these changes
might make the average national yields decline, due to the growing influence of the sandy areas.
However, the cumulative increase of the cultivated area in these regions, including Zuidwest Brabant,
is 9,193 ha from 1981 to 2010, which is 8% of the average total national area. This means that the
influence of this area change is a only 1.4% decrease in the national yield over the period of 1981 to
2010. Calderini & Slafer (1998) also concluded that changes in national yields per ha around the
world were not caused by changes in area.
4.3 Climate effects
4.3.1 Changes in climate
The CO2 concentrations in the air shows an accelerated increase (Fig. 3.3). This is in agreement with
findings of IPCC assessments (Forster et al., 2007) which found that the rate of increase of CO2 from
1995 to 2005 was the highest rate during any decade since 1950 until then.
Average annual and seasonal temperatures in the Netherlands increase all over the country (Fig. 3.4).
This is confirmed by the KNMI in its report on the state of the climate in the Netherlands in 2008
(Kattenberg, 2008). Kattenberg (2008) states that temperatures in the Netherlands and a large part
of western Europe increased twice as fast as the world average temperature since 1950. Specific
reasons for this in the Netherlands are the trend towards a more westerly wind in late winter and
early spring which brings in relatively warm air from above the sea and an increase in global radiation
in spring and summer. Bresser et al. (2005) state that the prevailing wind directions are hard to
predict on the long term, so current changes might be altered in the future. Over the period of 1950
to 2008 the increase in spring and autumn temperatures is respectively 2.8 and 1.8 times faster than
the global average increase (Bresser et al., 2005). The difference between spring and autumn occurs
because the previously mentioned processes, which contribute to the faster increase, occur in the
first half of the year. This can be important for the effect of temperature changes on plant growth.
One of the reasons for higher temperatures is increasing incoming radiation as was also described in
the results (Fig. 3.5). The radiation increased during the whole growing period of winter wheat and
during the period from April to July. Kattenberg (2008) also mentions this increase and the reasons
for it. Until about 1985 there was a small decrease of radiation in spring and summer, after that
there was a strong increase. The first reason for increase in incoming global radiation is the
brightness of the atmosphere. Until the 1980’s the amount of aerosols in the air increased, from the
second half of the 1980’s the air became cleaner again, so more solar radiation reached the ground.
The other reason which is more important in the long run is the change in overcast. In spring and
summer the overcast is reducing with warmer easterly and southerly wind. Also for Denmark
(Petersen et al., 2010) and the UK (Knight et al., 2012) increases in sunshine hours were reported.
Due to the increase in temperature and radiation the potential evapotranspiration of winter wheat
increases in the Netherlands (Fig. 3.6), despite the slight reduction in average wind speed mentioned
by Kattenberg (2008). Since there is no significant increase in average precipitation between April
and June (Fig. 3.7), precipitation deficit would logically go up. However due the large variation in
average precipitation from April to July there is no significant trend in average precipitation deficit in
47
late autumn and early summer. With respect to this Kattenberg (2008) found no trend in maximum
precipitation deficit between 1906 and 2007.
4.3.2 Effects of changes in climate
The negative correlation between average temperature during the growing season and winter wheat
yields in the Netherlands (Table 3.2) is a relation that is often found for wheat in Western Europe
(Petersen et al., 2010). The most likely explanation for this is that due to higher temperatures the
growing period shortens (Petersen et al., 2010; Knight et al., 2012). This means that there is also less
time for intercepting radiation which results in lower yields. Another explanation mentioned by
Petersen et al. (2010) is that in years with high temperatures there is more radiation and less rain
which leads to water shortage during anthesis. However this explanation seems not very plausible
since there are low correlations between average temperature during the growing season and
rainfall in spring and early summer (Table 3.1) and rainfall is negatively correlated with yield while
precipitation deficit has a positive influence (Table 3.2). Since the regression model includes the
average temperature over the growing period and not only from April or June to July, it seems that
temperatures from autumn to spring were also important. With respect to this Kristensen et al
(2011) found that winter temperatures in Denmark significantly correlated with yield. On the one
hand they noticed the abovementioned negative effect of high winter temperatures on the growing
period. On the other hand they recorded a negative effect of very low winter temperatures on yield,
probably caused by frost damage to the plants or drought problems due to reduced root
development in winter. The reducing effect of frost was also found by Knight et al. (2012), who
recorded a negative impact of the number of frost days on winter wheat yields. Kristensen et al
(2011) also found a negative effect of temperature on winter wheat yields in summer. Since this
effect was higher on sandy soils it is likely related to water shortage during grain filling, because
these soils have a lower water holding capacity.
The positive correlation between precipitation deficit and winter wheat yields cannot be a direct
effect, since normally precipitation deficit means water shortage. However, the correlations show
that precipitation deficit is highly correlated with rainfall as well as radiation. Therefore a high
precipitation deficit could stand for low amounts of rainfall or high amounts of radiation which could
lead to increasing yields. The negative correlation between the cumulative rainfall from April to July
and wheat yields in Hollands/Utrechts weidegebied confirm the explanation that low amounts of
rainfall can be improving yields. Similar relations between rainfall were found for winter precipitation
in Denmark (Petersen et al., 2010). Petersen et al. (2010) state that the negative effect of winter
precipitation can be caused by low nitrogen availability due to leaching or reduced root development
due to drainage problems. Negative impacts of summer rainfall on yields could be increases in
diseases, like Fusarium spp. which develop faster under humid circumstances, damage due to lodging
and harvest losses on soils with bad drainage. Drainage problems are a likely explanation for the
findings in the Netherlands, since the negative correlations with rainfall were not found in sandy
areas. Chloupek et al.(2004) and Brown (2013) also found high wheat yields in dry and warm
summers in the Czech Republic and Scotland respectively. Besides that they measured that there was
more sunshine during these summers, supporting the reasoning that high precipitation deficit is
related to high levels of radiation. The results show that apparently drought is not a big problem in
the Netherlands (Table 3.2). Kristensen (2011) and Petersen (2010) concluded the same for Denmark,
although it is important to notice that winter wheat on sandy soils in Denmark is often irrigated.
48
4.3.3 Modelling
4.3.3.1 Modelling assumptions and choices
In this model the relation between the CO2 concentrations in the air and the LUE an arbitrary
function generation (AFGEN) function was used. This function does linear interpolation, while a
smoothing spline interpolation might be a more realistic representation of the relation in reality,
since it does not contain sudden changes in the slope of the relation at the interpolation points.
In the spring wheat model reallocation was simulated by starting allocation of assimilates to the
grains before anthesis, which is not in accordance with reality. To avoid this the reallocation module
was inserted in this version of the model. However, the development stage at which grain filling
started was chosen too late (zadoks scale: 71, feekes scale: 10.54; Kernel watery ripe). Zadoks scale
65 (Anthesis half-way) might have been more appropriate since the data from Groot en Verberne
(1991) showed that the wheat crop already had grain weight when it reached zadoks scale 65.
Due to the late timing of the start of grain filling, reallocation had to start before DVS =1 to simulate
the measured trend in grain biomass build up.
The validation data show that there is not a very high correlation between observed and simulated
yields, especially not if the outliers to the upper right are not taken into account (Fig. 3.11). This is of
course very important, since a model that does not represent reality very well is a weak basis to build
conclusions on. There are two possible reasons for the low correlation. Firstly, the assumption that
the validation data represents optimal potential crop growth and yield, might be wrong. This could
be the case, since for all data from Darwinkel (1994; 1985) it is not known if disease control was used
or if crop yields were reduced in any way. Besides that, there were differences in yields of crops
grown at the same location and same sowing dates. As explained before the lower yields were
removed in those cases, since they clearly did not represent optimal yield. However, the fact that
those yields were reduced, imply that the highest yields, which were kept as validation data, could
also be non-optimal.
The second reason is of course the model itself. The model seems to be very inaccurate for low or
high plant densities. For in the graph (Fig. 3.11) the yields from crops sown at densities between 250
and 400 plants m-2 are scattered much more around the 1:1 line, than those from higher or lower
densities, which are all below the 1:1 line. Besides that, the yields from crops sown in September are
also all at the right side of the 1:1 line. This suggests that the model underestimates the positive
effect of early sowing on yields. These limitations of the model should be kept in mind while judging
the outcomes. Although these outcomes have not to be completely rejected, it might be necessary to
focus more on the global trends than on very specific details.
To evaluated the effect of individual weather factors, simulations where carried out with averaged
daily weather data over 30 years. This is not without consequence. Since the data is average all
extreme daily values are weakened. For temperatures extremely high or low temperatures mostly
result in suboptimal growing conditions and thus reduced yields. So the simulations with averaged
weather data might in general give an overestimation of yields compared to reality. This might
explain why cumulative increase in biomass of all individual factors is higher than the biomass
increase due to the combination of all three factors in this research.
49
4.3.3.2 Modelling results
Since development of winter wheat is temperature dependent, the measured increase in average
temperature over the growing season leads to faster development of the crop. This effect comes
back in nearly every study on the effect of climate on wheat yields (Nonhebel, 1993; Olesen et al.,
2000; Ghaffari et al., 2002; Wolf et al., 1996; Estrella et al., 2007; Olesen and Bindi, 2002;
Schapendonk et al., 1998; Børgesen and Olesen, 2011; Tonkaz et al., 2010; Petersen et al., 2010).
With a temperature increase of 1.3 to 1.6 oC, harvest dates became 12 to 14 days earlier over 30
years. This effect is stronger than the 4.4 to 5.2 day per oC estimated by Estrella et al. (2007) in
Germany, the 6 and 10 days for 0.9 and 1.5 oC respectively found by Ghaffari et al. (2002) and the 15
to 20 days for 3 degrees mentioned by Nonhebel (1993). An even stronger effect was mentioned by
Petersen et al. (2010) for Denmark, namely 5% per 1 degree change.
The shift in phenology of winter wheat in the Netherlands to earlier harvest leads to a reduction in
interception of radiation before anthesis in two different ways (Nonhebel, 1993). Firstly the growing
season and thus the time to intercept radiation becomes shorter. Secondly the growing period shifts
more towards winter during which radiation levels are lower. This process results in lower yields
(Olesen et al., 2000; Tonkaz et al., 2010; Olesen and Bindi, 2002; Petersen et al., 2010). It is important
to notice that there was no significant change in global radiation during the period of grain filling.
Besides that the shift in phenology means that the period of grain filling is ‘timed’ more around the
longest day. So the radiation levels during this period might even increase.
Besides development, temperature also affects assimilation rate (Nonhebel, 1993). If mean daily
temperatures are lower than 12 oC the LUE is suboptimal. In the period before anthesis, spring, there
are many suboptimal days resulting in an average LUE correction factor for temperature which is
lower than 1. This study shows that this LUE correction factor is increasing over the years due to
higher mean temperatures. This results in a higher LUE and higher assimilation and growth. So higher
temperatures during cold phases of the growing period are increasing growth and yield
(Schapendonk et al., 1998). This was also found for Denmark by Olesen et al. (2000) and for
simulated winter wheat in the UK by Wolf et al. (1996). Higher average temperatures in winter can
also be positive because it indicates less frost damage; however frost damage was not included in
this study. Olesen et al. (2000) found that higher yields in Denmark due to higher winter
temperatures was most likely the effect of better establishment of the leaf area instead of better
winter survival.
In this study the positive and negative effects of higher mean temperatures during the growing
season of winter wheat are apparently counterbalancing each other, since there is no significant
change in grain yields due to temperature change. Kristensen et al. (2011) also found that
temperatures from April to June did not affect yields. Olesen et al. (2000) show that winter wheat
yields in Denmark on loamy soils slightly increase or stay stable with an increase of mean
temperature up to 3 oC and start decreasing if temperature becomes higher than 3 oC. Nonhebel
(1993) stated that the negative effect of temperature on development would be overruling other
effects and Schapendonk et al. (1998) and Nonhebel (1993) even found this for the Netherlands in an
analyses of climate change effects on winter and spring wheat yields in the future respectively.
Therefore in the future the balance in the effects might become negative since the beneficial effect
on LUE will become saturated and the effect on phenology will continue.
50
The increase of incoming global radiation over the growing season, especially before anthesis, leads
to increase assimilate production and thus biomass production of about 20% in 30 years. As
mentioned earlier part of the radiation change is due to reduction in human emitted aerosols, which
can be expected to sustain. A more important influence however is the effect of warm winds on
cloudiness. It is not clear how the overcast will change in the future which makes it hard to estimate
the changes in radiation in the coming decades.
51
4.3.4 CO2 effect
The increase in CO2 concentrations in the air causes a higher light use efficiency (Olesen and Bindi,
2002; Petersen et al., 2010; Børgesen and Olesen, 2011) and thus increased biomass production
before and during grain filling, which is found in many studies with different models (Wolf et al.,
1996; Gervois et al., 2008; Nonhebel, 1993).
The increase in yield is higher than many other studies indicate namely ± 10% with a CO2
concentration rise of 50 ppm in 30 years. Nonhebel (1993) estimated an increase of 40 to 50% with a
doubling of CO2 levels from 350 to 700 ppm. Bresser states that a doubling of CO2 concentrations can
increase yields with 15 to 20% and Tonkaz et al. (2010) estimate a yield increase of 150 kg per ha per
40 ppm increase in CO2 concentrations. Based on results from Olesen & Bindi (2002), Petersen et al.
(2010) estimate the effect of the 30 ppm increase in CO2 concentrations from 1990 to 2008 in yields
to be about 4% and Knight et al. (2012) conclude that a 50 ppm increase in CO2 concentration
results in 6% extra yield.
There are two possible reasons for the difference in yield response between this study and others.
Firstly the relation between LUE and CO2 concentrations that was used in this study is nonlinear,
which means that the LUE increase due to higher CO2 concentrations is slowing down with higher
concentrations. The nonlinear response curve of yield to CO2 levels is in agreement with results from
Olesen & Bindi (2002) and Ko et al. (2010). Since many of the above mentioned studies look at the
effect of CO2 increase in the future, when CO2 levels are higher, the relative effect of CO2 increase on
LUE is lower in those studies since the effect is reducing.
Secondly the response curve of LUE to CO2 concentrations in this study might have been
overestimating the stimulating effect of increasing CO2 levels. The response curve that is used
increases the initial LUE with 11% and the saturated LUE with 60% for a doubling of CO2 from 360 to
720 ppm, which leads to a LUE increase of 35%. This response curve is based on Supit et al. (2012),
who based their LUE adjustments on findings from the 1980’s by for instance Cure & Acock (1986).
Later Long et al. (2006) carried out free-air concentration enrichment (FACE) experiments in which
they found wheat photosynthesis responses to CO2 increase which were almost 40% lower than
those of Cure & Acock (1986), who found a 21% increase in photosynthesis if CO2 concentrations
were elevated to 550 ppm. This might explain the difference with other estimates and simulation
results. Wang et al. (2013) also found that yield increases due to elevated CO2 were lower in FACE
experiments and that many physiological factors affect the CO2 effect on plant growth. They suggest
that more detailed FACE experiments will have to create more clarity on the relation between CO2
and wheat yields.
Even though the effect might be overestimated, still it is clear that the CO2 concentration changes
over the past 30 years have led to a non-linearly increasing potential yield of winter wheat.
The decrease in HI due to higher CO2 concentrations in the air, found in this study, was also found by
Tonkaz et al. (2010) for Bulgaria. This might be due to the fact that the extra biomass production due
to the higher LUE is mostly produced during the vegetative phase of the crop growth, since in spring
there are much more days with suboptimal temperatures.
52
4.3.5 Combined effect
The overall effect of changes in weather on winter wheat yields in the Netherlands from 1981 - 2010
was positive. The potential yield of aboveground biomass and grain has increased. Grain yields
increased with 26 to 39% depending on the sowing date and variety. Petersen et al. (2010) estimate
that there was a negligible effect of weather on winter wheat yields the past decades, but that CO2
increases lead to a higher potential yield in Denmark. For the UK, Knight et al. (2012) state that there
was a positive effect of both CO2 and weather on winter wheat yields up to 1996, after that only CO2
changes continued to be positive while weather changes were negative for potential yields. Overall
the negative influence of weather after 1996 was bigger than the positive effect of CO2 increase in
the UK. Another study in the UK Ghaffari et al. (2002) found that a temperature increase of 0.9 oC
combined with a CO2 level of 400 ppm would give an 8% increase in grain yields. Supit et al. (2010)
found increasing potential yields for the Netherlands, Belgium, Germany and the UK, with 50, 40, 40
and 60 kg ha-1 year-1 from 1976 to 2005, while only incorporating radiation and temperature changes.
Although potential grain yields are increasing the response is non-linear and after the year 2000 the
increase has almost stagnated. This is mostly due to the nonlinear response of photosynthesis rates
to CO2 increases.
Changes in CO2, global radiation and temperature lead to a decline in harvest index of winter wheat.
None of the single atmospheric factors showed a significant negative effect on harvest index which
was as big as the combined effect. This suggests that the separate factors negatively influenced the
HI, although not significantly, and that the combination of these small effects resulted in a significant
decline of HI. The decline in HI probably occurs because the period before grain filling benefits more
from increased radiation, CO2 and a higher LUE than the period after anthesis. Tonkaz et al. (2010)
also found a negative effect of climate on the HI of a simulated winter wheat crop. This decline in HI
suggests that the extra assimilates produced due to atmospheric changes do not all result in extra
grain yield due to a sink limitation. With respect to this Wang et al. (2013) recommend wheat
breeders to focus on increasing the sink of wheat varieties.
4.4 Drivers of yield trends The purpose of this research was to find out what has driven yield changes in winter wheat over the
past 30 years. In figure 5.2 the yield trends at different levels are visualized. The first year of our
investigation, 1981, is taken as the point of reference to show yield changes. The change in winter
wheat yield from variety trials is derived from Rijk et al. (2013). This change can be split up in a
genetic yield change due to breeding and a yearly yield change due to other factors. In the present
study the yield change due to changes in weather, including CO2, temperature and radiation, have
been investigated. Furthermore, the trends in on-farm yields since 1981 or since before 1978
described in section 3.1.2 are included.
53
The yield change over 30 years due to genetic improvements and changes in weather could have
been five ton per ha. The change in the actual yields in the variety trials over that same period is
about 3.8 ton per ha and on farm this is only three tons. So, apparently two yields gaps have been
widening. Firstly, there was an increase in the yield gap of ± 1.3 ton per ha between potential yields
and yields in the variety trials from 1981 to 2010. Using the G x E x M concept, this means that the
environment must have caused this increase in the yield gap, since G is incorporated in both yield
trends and management of the variety trials has not changed. Secondly, the yield gap between yields
of the variety trials and on-farm yields increased with 1.7 ton per ha over the same period. Rijk et al
(2013), found a higher increase in on-farm yields, probably, due to the fact that they investigated
marine clay areas, which have a higher yield than the average national yield in this study (App. I: Fig.
I.3). Therefore, they found a smaller increase in yield gap between winter wheat on farms and in
variety trials, namely 1.2 ton per ha in thirty years. Weather influences both farm en variety trial
yield trends similarly. The genetic increase should also be the same, because farmers are likely to use
the latest varieties. Therefore the difference between farm yields and yields from trials is probably
mostly attributable to changes in management of the farmers. There are many different processes
that might have contributed to the increasing yield gaps.
4.4.1 Extreme events
In this study the direct effect of CO2 and weather on growth and development of winter wheat have
been addressed. There are, however, more effects of changes in weather patterns that have not
been addressed. Firstly extreme weather events can damage crops and thus crop yields Olesen
(2002; Bresser et al., 2005). Schaap et al. (2011) mention the following events as damaging for winter
wheat (Table 5.1):
Figure 5.2 Changes in winter wheat yield components (15% moisture) in the Netherlands from 1981 to 2010.
Yield trends from variety trials were based on Rijk et al.(Rijk et al., 2013)
-4
-3
-2
-1
0
1
2
3
4
5
6
1980 1985 1990 1995 2000 2005 2010
Gra
in y
ield
ch
ange
(M
g h
a-1
)
Year
Genetic + Weather
Genetic + year(variety trials)Weather
Genetic (Variety trials)
On-farm since <1978
On-farm since 1981
Other factors since<1978Other factors since1981
54
Schaap et al. (2011) found that the frequency of damaging extreme events will increase in the future
and this is confirmed by Kattenberg (2008). If this increase has already been started this could have
already started to negatively influence yields over the past 30 years. The measured negative
correlation between precipitation and wheat yields also indicates that damage due to wetness or
humidity in late spring and early summer have been reducing crop yields from 1981 to 2010.
Analyses in other European countries also indicate that damaging events can be significantly
influencing winter wheat yields. Knight et al. (2012) found that increasing frost in January probably
has a negative effect on winter wheat yields.
Kristensen et al. (2011) suggest that negative influences of low winter temperatures or high summer
precipitation can be due to frost damage or problems with harvesting and septoria and fusarium
diseases. In warmer countries like France, heat stress during grain filling is an important damaging
event (Brisson et al., 2010).
4.4.2 Ozone
Other processes that might have influenced winter wheat yields in the Netherlands are ground level
O3 concentrations and UV-B radiation. According to CBS et al. (2013) the exposure of vegetation to
damaging concentrations of ground level ozone declined from 1990 to 2000. After 2000 the decline
stopped. Because measurements started in the 1990’s it is not clear what happened before 1990,
although it is assumable that ozone concentrations were higher since regulations for ozone reduction
were introduced in the late 1980’s (Kattenberg, 2008). Kattenberg (2008) states that although local
emissions of ozone enhancing gasses have been reduced, background ozone concentrations are
increasing due to increases of emissions in Eastern-Asia and climatic interactions. For crop damage
peak concentrations are more important than average concentrations. Therefore the reduction in
local emissions has been more important until now and it is likely that ozone damage did not
increase over the past 30 years. For Denmark (Petersen et al., 2010) and the UK (Knight et al., 2012),
similar results were found. Besides the direct effect of reduction in peak ozone concentrations, there
is also an effect of O2 on O3 damage. Increasing O2 concentrations lead to less uptake of O3 and thus
to less internal damage(Petersen et al., 2010; Schapendonk et al., 1998). It is not clear what the
magnitude of this effect on yield has been, but it strengthens the idea that ozone damage has not
increased over the past 30 years.
Table 5.1 Damaging weather events for winter wheat in the Netherlands based on Schaap et al. (Schaap et al., 2011)
Climate factor Vulnerable period Impact on Crop
Wet field Oct - Dec Delayed planting date Frost-thaw Nov - Mar Root damage Drought Jun - Aug Water shortage Sustained wet Apr - May Leaf blotch Septoria tritici damage Sustained humid May - Jul Seedling blight Fusarium spp. and Septoria nodorum
damage Wind and rain surges May - Aug Lodging, inability to harvest Sustained wet Jul - Sep Inability to harvest
55
4.4.3 UV-B
UV-B radiation (wavelength: 280-315 nm) has many effects on plant morphology and physiology
(Petersen et al., 2010). There is not much specific data on UV-B concentrations from the past in the
Netherlands and the rest of Europe (Petersen et al., 2010; Knight et al., 2012). Therefore damages
due to increases in UV-B concentrations can neither be excluded nor verified (Petersen et al., 2010;
Knight et al., 2012).
4.4.4 Management
Besides genetic and environmental factors, management is the third aspect influencing farm crop yields. Rijk et al. (2013), Timmer (2012) and Veeman (2012) state that a shift to later sowing of winter wheat has had a negative effect on winter wheat yields. The harvest time of sugar beets, the crop often preceding wheat in the rotation, has become later during the past decades (Veeman, 2012; Timmer, 2012), resulting in later sowing of winter wheat. Another reason for later sowing is the increase in average farm size which can lead to challenges with timing of the crop management (Rijk et al., 2013; Peltonen-Sainio et al., 2009). Later sowing leads to lower yields since the wheat crop is less established before temperature drops and when growth continues in spring (Ghaffari et al., 2002), while the harvest date is almost similar to early sown winter wheat due to vernalization and photosensitivity effects. The simulated reduction in grain yield due to late sowing is slightly higher than reductions in yield found by Habekotte (1989) in long term trials, namely 7% and 5% for the present study and Habekotte (1989), respectively. However, the late sowing date was not included in the analyses to quantify the effect of late sowing, but to check if there was an interaction between late sowing and climatic changes. In addition to the direct effect of late sowing on crop yields, late sowing also increases the risk of wet circumstances during sowing which can reduce the quality of the seedbed and thus germination. Changes in management might also be induced by price changes (Rijk et al., 2013; Veeman, 2012; Timmer, 2012). According to Rijk et al. (2013) prices of winter wheat decreased from the late 1980’s to ± 2007, mainly because of European policy changes. These price reductions and the direct EU payments which became based on cultivation probably resulted in reduced incentive for farmers to push for high yields (Brisson et al., 2010; Peltonen-Sainio et al., 2009; Himanen et al., 2013). European policy also has affected input rates of chemical fertilizers and animal manure (Peltonen-Sainio et al., 2009; Petersen et al., 2010). This might have affected winter wheat yields as well, however, Timmer (2012) and Veeman (2012) do not expect current nitrogen application rates to be limiting. For Denmark (Petersen et al., 2010) and the UK (Knight et al., 2012) yield reductions were estimated to be respectively 0.4 and 0.2 ton per ha due to reductions in N fertilization, other nutrients did not reduce winter wheat yields. The change in N application from three to two applications by many Dutch farmers could also have reduced grain yields (Timmer, 2012; Veeman, 2012). Recently a Dutch agricultural consultancy involved in soil sampling stated that due to the limitation of animal manure application micronutrient deficiencies have started to manifest since 1996 (Anonymous, 2013). Although it is not clear what the specific situation for winter wheat is, this might have affected also wheat yields.
Figure 5.3 Annual grain prices of fodder, milling and baking quality in
euro per kg from 1980 to 2010 in the Netherlands. Source (LEI, 2014)
0
50
100
150
200
250
300
1980 1990 2000 2010
Gra
in p
rice
(€
10
0 k
g-1)
Year
Wheat -FodderqualityWheat -MillingQualityWheat -Bakingquality
56
Another important aspect of management is soil compaction. Due to the use of heavy machinery for root crop harvest in autumn and (increased) slurry application in spring soil compaction may affect crop yields (Rijk et al., 2013; Petersen et al., 2010; Knight et al., 2012). Slurry application not only affects the crop through soil compaction but it can also directly damage vulnerable crops in spring (Petersen et al., 2010). Petersen et al. (2010) estimate that both these processes have reduced yields with about 0.1 ton per ha from 1990 to 2006. Additional negative influences on winter wheat yields might have been lower investments in soil tillage and mice problems (Veeman, 2012) or higher occurrence of fusarium infections (Timmer, 2012).
57
5 Conclusions & Recommendations
5.1 Main findings Crop growth simulations suggest that weather and CO2 changes positively affected potential winter
wheat yields over the past 30 years, with an estimated average increase of about 31% for all
‘varieties’ and sowing dates. The simulated increase consists of a 10% increase due to an increase
radiation and a 21% increase due to a rise in CO2 concentrations, while the increase in temperature
did not have a significant effect. The increase in potential yields diminished towards the end of
studied time span, due to an assumed non-linear response of photosynthesis to increasing CO2 levels.
The simulated response was similar for normal and late sown winter wheat and for old and new
varieties, although the magnitudes of the effects differed. The simulated harvest index of winter
wheat decreased linearly from 0.51 to 0.47 on average, due to changes in temperature radiation and
CO2. Based on the simulated increase due to climate changes in combination with earlier found
genetic improvements and lagging behind of increases farm yields, it becomes clear that there must
have been other negative influences on winter wheat yields, which have to be further explored.
5.2 Recommendations Since the response of yields to CO2 increase seems to be higher than other studies have found,
further research on this relation should be done to better estimate the magnitude of the effect.
Although it is likely that UV-B radiation increased over the past 30 year, since total global radiation
increased, the magnitude of this increase is not known. Therefore this should be investigated in more
detail. The regression analysis on the effects of climate on winter wheat yields in the Netherlands
carried out in this study, were not very detailed with respect to the variability of weather factors
within the year. To get more insight in the effects of extreme weather events on winter wheat crops
and their management can be further explored using regression or frequency analyses with weekly
or daily weather data. Besides that, the main third aspect of crop production, management, also has
to be investigated to analyse the effects of a.o. fertilization, rotation, timeliness, soil compaction,
disease control, sowing time and tillage.
59
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65
Figure I.1 Average area of winter wheat in (a) ‘Bouwhoek & Hogeland’ , ‘Veenkoloniën & Oldambt’ and
‘Zuidwestelijk akkerbouwgebied’, (b) ‘Westelijk Holland’,‘ IJsselmeerpolders’ and ‘Rivierengebied’ and (c)
‘Zuid-Limburg’, ‘Zuid-West Brabant’ and ‘Noordelijk weidegebied’the Netherlands from 1981 until 2010.
Data series marked with an asterisk do not show a significant trend (P < 0.05). In equations x = year - 1981.
Appendix I Results
y = 8.007 - 0.0498x R² = 0.12
y = 23.45 - 0.917x + 0.02111x2 R² = 0.63
R² = 0.86 y = 1.804 + 0.11234x2
0
5
10
15
20
25
30
1980 1985 1990 1995 2000 2005 2010 2015
Are
a (1
00
0 h
a)
Year
Westelijk Holland IJsselmeerpolders Rivierengebied
y = 3.659 + 0.1441x - 0.003810x2 R² = 0.58
y = 1.0544 + 0.01541x R² = 0.17
R² = 0.11 y = 2.832 + 0.0393x
0
1
2
3
4
5
6
1980 1985 1990 1995 2000 2005 2010 2015
Are
a (1
00
0 h
a)
Year
Zuid-Limburg Zuidwest Brabant Noordelijk weidegebied
y = 51.05 - 1.306x + 0.0380x2 R² = 0.16
0
10
20
30
40
50
60
1980 1985 1990 1995 2000 2005 2010 2015
Are
a (1
00
0 h
a)
Year
Bouwhoek en Hogeland Veenkoloniën en Oldambt Zuidwest. akkerbouwgebied
a
b
c
66
Figure I.2 Average area of winter wheat in (a) ‘Zuidelijk veehouderijgebied’, ‘Hollands-Utrechts
weidegebied’ and ‘Oostelijk veehouderijgebied’ and (b) ‘Waterland & Droogmakerijen’ and ‘Centraal
veehouderijgebied’ the Netherlands from 1981 until 2010. In equations x = year - 1981.
y = 2.644 + 0.1276x R² = 0.54
y = 0.0729 + 0.00231x + 0.000324x2 R² = 0.80
y = 0.846 + 0.1513x R² = 0.80
0
1
2
3
4
5
6
7
8
1980 1985 1990 1995 2000 2005 2010 2015
Are
a (1
00
0 h
a)
Year
Zuidelijk veehouderijgebied Hollands/Utr. weidegebied Oostelijk veehouderijgebied
y = 1.1922 - 0.01204x R² = 0.22
y = 0.0181 + 0.01211x R² = 0.72
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1980 1985 1990 1995 2000 2005 2010 2015
Are
a (1
00
0 h
a)
Year
Waterland & Droogmakerijen Centraal veehouderijgebied
a
b
67
y = 5.677 + 0.0656x R² = 0.37
y = 5.677 + 0.0656x R² = 0.38
0
2
4
6
8
10
12
1980 1985 1990 1995 2000 2005 2010 2015
Pro
du
ctiv
ity
(to
n h
a-1)
Year
Centraal veehouderijgebied IJsselmeerpolders
y = 6.251 + 0.0651x R² = 0.47
y = 6.650 + 0.0905x R² = 0.63
0
2
4
6
8
10
12
1980 1985 1990 1995 2000 2005 2010 2015
Pro
du
ctiv
ity
(to
n h
a-1)
Year
Zuidelijk veehouderijgebied Zuid-Limburg
y = 7.239 + 0.0663x R² = 0.52
y = 5.978 + 0.0787x R² = 0.49
0
2
4
6
8
10
12
1980 1985 1990 1995 2000 2005 2010 2015
Pro
du
ctiv
ity
(to
n h
a-1)
Year
National Oostelijk veehouderijgebied
y = 7.698 + 0.0641x R² = 0.41
y = 6.857 + 0.0736x R² = 0.49
0
2
4
6
8
10
12
1980 1985 1990 1995 2000 2005 2010 2015
Pro
du
ctiv
ity
(to
n h
a-1)
Year
Rivierengebied Zuidwestelijk akkerbouwgebied
Figure I.3 National and regional winter wheat (Triticum aestivum L.) yield trends from 1981 until 2010 in the Netherlands. In equations x = year - 1981.
a
c d
b
68
y = 16.39 + 0.0604x R² = 0.19
R² = 0.43 y = 13.36 + 0.0661
y = 8.256 + 0.0510x R² = 0.26
5.0
7.0
9.0
11.0
13.0
15.0
17.0
19.0
1980 1985 1990 1995 2000 2005 2010 2015
Ave
rage
tem
per
atu
re (
oC
Cel
ciu
s)
Year
y = 16.24 + 0.0547x R² = 0.19
R² = 0.45 y = 13.23 + 0.0643x
y = 8.893 + 0.0525x R² = 0.27
5.0
7.0
9.0
11.0
13.0
15.0
17.0
19.0
1980 1985 1990 1995 2000 2005 2010 2015
Ave
rage
tem
per
atu
re (
oC
Cel
ciu
s)
Year
y = 15.33 + 0.0620 R² = 0.20
y = 12.59 + 0.0587x R² = 0.33
y = 7.649 + 0.0433 R² = 0.15
5.0
7.0
9.0
11.0
13.0
15.0
17.0
19.0
1980 1985 1990 1995 2000 2005 2010 2015
Ave
rage
tem
per
atu
re (
oC
Cel
ciu
s)
Year
y = 14.97 + 0.0605x R² = 0.20
y = 12.21 + 0.0583x R² = 0.34
y = 7.344 + 0.0497x R² = 0.18
5.0
7.0
9.0
11.0
13.0
15.0
17.0
19.0
1980 1985 1990 1995 2000 2005 2010 2015
Ave
rage
tem
per
atu
re (
oC
Cel
ciu
s)
Year
Vlissingen
Eelde
Eelde Twenthe
Figure I.4 Trends in mean daily temperature averaged over a period of June to July (green), April to July (blue) and October to July (red) from 1981 to 2010 at four
weather stations in the Netherlands. In equations x = year - 1981.
69
y = 1935 + 7.98x R² = 0.31
y = 2776 + 7.28x R² = 0.20
1500
2000
2500
3000
3500
4000
1980 1985 1990 1995 2000 2005 2010 2015
Glo
bal
rad
iati
on
(M
J m
-2)
Year
y = 1753 + 144x R² = 0.36
y = 2498 + 15.5x R² = 0.35
1500
2000
2500
3000
3500
4000
1980 1985 1990 1995 2000 2005 2010 2015
Glo
bal
rad
iati
on
(M
J m
-2)
Year
y = 2061 + 7.94x R² = 0.26
y = 2896 + 9.52x R² = 0.25
1500
2000
2500
3000
3500
4000
1980 1985 1990 1995 2000 2005 2010 2015G
lob
al r
adia
tio
n (
MJ
m-2
)
Year
y = 1885 + 8.77x R² = 0.21
y = 1614 + 9.14x R² = 0.23
1500
2000
2500
3000
3500
4000
1980 1985 1990 1995 2000 2005 2010 2015
Glo
bal
rad
iati
on
(M
J m
-2)
Year
Eelde
Figure I.5 Trends in incoming global radiation accumulated over a period of April to July (blue) and October to July (red) from 1981 to 2010 at four weather stations in
Netherlands. In equations x = year - 1981.
Twenthe
0
0
0
0
Maastricht Vlissingen
70
y = 294.6 + 1.65x R² = 0.35
y = 314.1 + 1.78x R² = 0.37
200
250
300
350
400
1980 1985 1990 1995 2000 2005 2010 2015C
um
ula
tive
eva
po
tran
spir
atio
n (
mm
)
Year
y = 309.5 + 1.59x R² = 0.30
y = 329.9 + 1.74x R² = 0.33
200
250
300
350
400
1980 1985 1990 1995 2000 2005 2010 2015
Cu
mu
lati
ve e
vap
otr
ansp
irat
ion
(m
m)
Year
y = 266.3 + 2.42x R² = 0.32
y = 281.0 + 2.73x R² = 0.36
200
250
300
350
400
1980 1985 1990 1995 2000 2005 2010 2015
Cu
mu
lati
ve e
vap
otr
ansp
irat
ion
(m
m)
Year
y = 278.9 + 1.67x R² = 0.25
y = 296.9 + 1.83x R² = 0.27
200
250
300
350
400
1980 1985 1990 1995 2000 2005 2010 2015Cu
mu
lati
ve e
vap
otr
ansp
irat
ion
(m
m)
Year
Twenthe Eelde
Vlissingen
Figure I.6 Trends in reference (blue) and actual (green) evapotranspiration of winter wheat (Triticum aestivum L.) accumulated over a period of April to July from 1981
to 2010 at four weather stations Netherlands. At Twenthe data was not available before 1987. In equations x = year - 1981.
Maastricht
0 0
0
0
71
Eelde
-200
-100
0
100
200
300
400
1980 1985 1990 1995 2000 2005 2010 2015
Pre
cip
itat
ion
def
icit
& R
ain
fall
(mm
)
Year
-200
-100
0
100
200
300
400
1980 1985 1990 1995 2000 2005 2010 2015
Pre
cip
itat
ion
def
icit
& R
ain
fall
(mm
)
Year
-200
-100
0
100
200
300
400
1980 1985 1990 1995 2000 2005 2010 2015
Pre
cip
itat
ion
def
icit
& R
ain
fall
(mm
)
Year
-200
-100
0
100
200
300
400
1980 1985 1990 1995 2000 2005 2010 2015
Pre
cip
itat
ion
def
icit
& R
ain
fall
(mm
)
Year
Twenthe
Vlissingen Maastricht
Eelde
Figure I.7 Trends in rainfall and precipitation deficit of winter wheat (Triticum aestivum L.) accumulated over a period of April to July (rainfall [red], precipitation deficit
[green]) and June - July (precipitation deficit {blue]) from 1981 to 2010 at four weather stations in the Netherlands. For Twenthe no precipition deficit data was available
before 1987. In equations x = year - 1981.
72
Table I.1 Correlation coefficients between weather factors and CO2 for weather station Eelde, Netherlands over de period 1981 - 2010.
Actual evapotranspiration (April - July) 1.00
Reference evapotranspiration (October - July) 1.00 1.00 Global radiation (April - July) 0.98 0.98 1.00
Global radiation (October - July) 0.92 0.94 0.96 1.00 Precipitation deficit (April - July) 0.53 0.53 0.55 0.60 1.00
Precipitation deficit (June - July) 0.54 0.54 0.52 0.58 0.74 1.00 Rainfall (April - July) -0.11 -0.12 -0.15 -0.24 -0.91 -0.60 1.00
Average temperature (October - July) 0.33 0.33 0.23 0.17 -0.12 -0.16 0.30 1.00 Average temperature (April - July) 0.67 0.67 0.55 0.50 0.19 0.24 0.11 0.71 1.00
Average temperature (June - July) 0.76 0.78 0.69 0.69 0.36 0.59 -0.04 0.43 0.77 1.00 Year 0.52 0.54 0.50 0.51 0.14 0.06 0.10 0.46 0.61 0.48 1.00
CO2 0.54 0.56 0.52 0.53 0.16 0.07 0.08 0.46 0.61 0.48 1.00 1.00
Actu
al
evapo
transp
iration
(A
pril - Ju
ly)
Refe
rence
evapo
transp
iration
(O
ctob
er - July)
Glo
bal rad
iation
(A
pril - Ju
ly)
Glo
bal rad
iation
(Octo
ber - Ju
ly)
Precip
itation
deficit
(Ap
ril - July)
Precip
itation
deficit
(Jun
e - July)
Rain
fall (Ap
ril - Ju
ly)
Ave
rage te
mp
erature
(Octo
ber - Ju
ly)
Ave
rage
tem
peratu
re (Ap
ril - Ju
ly)
Ave
rage
tem
peratu
re (Jun
e - Ju
ly)
Year
CO
2
Legend
|Correlation| -
1.0 - 0.8
0.7 - 0.8
0.5 - 0.7
0.4 - 0.5
0.2 - 0.4
0 - 0.2
73
Table I.2 Correlation coefficients between weather factors and CO2 for weather station Maastricht, Netherlands over de period 1981 - 2010.
Actual evapotranspiration (April - July) 1.00
Reference evapotranspiration (October - July) 1.00 1.00 Global radiation (April - July) 0.97 0.98 1.00
Global radiation (October - July) 0.84 0.85 0.89 1.00 Precipitation deficit (April - July) 0.69 0.69 0.72 0.67 1.00
Precipitation deficit (June - July) 0.64 0.63 0.65 0.63 0.86 1.00 Rainfall (April - July) -0.48 -0.48 -0.52 -0.51 -0.97 -0.82 1.00
Average temperature (October - July) 0.45 0.46 0.37 0.24 0.08 -0.07 0.07 1.00 Average temperature (April - July) 0.76 0.76 0.63 0.47 0.32 0.24 -0.11 0.70 1.00
Average temperature (June - July) 0.80 0.80 0.72 0.64 0.53 0.64 -0.35 0.38 0.75 1.00 Year 0.61 0.63 0.58 0.48 0.29 0.16 -0.13 0.54 0.67 0.47 1.00
CO2 0.62 0.63 0.58 0.49 0.28 0.15 -0.12 0.53 0.66 0.47 1.00 1.00
Actu
al
evapo
transp
iration
(A
pril - Ju
ly)
Refe
rence
evapo
transp
iration
(O
ctob
er - July)
Glo
bal rad
iation
(A
pril - Ju
ly)
Glo
bal rad
iation
(Octo
ber - Ju
ly)
Precip
itation
deficit
(Ap
ril - July)
Precip
itation
deficit
(Jun
e - July)
Rain
fall (Ap
ril - Ju
ly)
Ave
rage te
mp
erature
(Octo
ber - Ju
ly)
Ave
rage
tem
peratu
re (Ap
ril - Ju
ly)
Ave
rage
tem
peratu
re (Jun
e - Ju
ly)
Year
CO
2
Legend
|Correlation| -
1.0 - 0.8
0.7 - 0.8
0.5 - 0.7
0.4 - 0.5
0.2 - 0.4
0 - 0.2
74
Table I.3 Correlation coefficients between weather factors and CO2 for weather station Vlissingen, Netherlands over de period 1981 - 2010.
Actual evapotranspiration (April - July) 1.00
Reference evapotranspiration (October - July) 1.00 1.00 Global radiation (April - July) 0.97 0.98 1.00
Global radiation (October - July) 0.90 0.91 0.95 1.00 Precipitation deficit (April - July) 0.65 0.66 0.71 0.67 1.00
Precipitation deficit (June - July) 0.47 0.47 0.50 0.45 0.84 1.00 Rainfall (April - July) -0.36 -0.38 -0.46 -0.44 -0.95 -0.83 1.00
Average temperature (October - July) 0.38 0.41 0.29 0.25 0.09 -0.13 0.06 1.00 Average temperature (April - July) 0.72 0.73 0.59 0.51 0.21 0.09 0.05 0.75 1.00
Average temperature (June - July) 0.78 0.78 0.69 0.61 0.47 0.50 -0.25 0.38 0.75 1.00 Year 0.57 0.59 0.53 0.52 0.23 0.06 -0.03 0.55 0.69 0.47 1.00
CO2 0.57 0.60 0.54 0.53 0.23 0.07 -0.04 0.54 0.68 0.47 1.00 1.00 A
ctua
l eva
po
tran
spira
tion
(Ap
ril - July)
Referen
ce eva
po
tran
spira
tion
(Octo
ber - Ju
ly)
Glo
ba
l rad
iatio
n
(Ap
ril - July)
Glo
ba
l rad
iatio
n
(Octo
ber - Ju
ly)
Precip
itatio
n d
eficit (A
pril - Ju
ly)
Precip
itatio
n d
eficit
(Jun
e - July)
Ra
infa
ll (Ap
ril - Ju
ly)
Avera
ge
temp
eratu
re
(Octo
ber - Ju
ly)
Avera
ge
temp
eratu
re (Ap
ril
- July)
Avera
ge
temp
eratu
re (Jun
e
- July)
Year
CO
2
Legend
|Correlation| -
1.0 - 0.8
0.7 - 0.8
0.5 - 0.7
0.4 - 0.5
0.2 - 0.4
0 - 0.2
75
0.0
0.2
0.4
0.6
0.8
1.0
1.2
-30.0 -20.0 -10.0 0.0 10.0 20.0 30.0 40.0
Ve
rnal
izat
ion
rat
e
Temperature (oC)
Original
Calibrated
0
0.2
0.4
0.6
0.8
1
1.2
-15.0 -10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0
Ph
oto
pe
rio
dic
fac
tor
Daylength (h d-1)
Original
Calibrated
0
0.2
0.4
0.6
0.8
1
1.2
0 0.5 1 1.5 2 2.5
SLA
co
rre
ctio
n f
acto
r
Development stage
Original
Calibrated
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
-20 -10 0 10 20 30 40 50 60
Re
lati
ve d
eat
h r
ate
of
leav
es
(d-1
) Temperature (oC)
Original
CalibratedVlissingen
0
0
Figure I.8 Original and calibrated relations between (a) day length (h d-1
) and the photoperiodic factor for development, (b) average day temperature (oC) and
vernalization rate, (c) development stage and specific leaf area correction factor and (d) average day temperature (oC) and relative death rate of leaves of winter wheat
(Triticum aestivum L.).
a b
c d
76
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 500 1000 1500 2000 2500
LUE
corr
ect
ion
fac
tor
CO2 concentration in the air (ppm)
0
0.2
0.4
0.6
0.8
1
1.2
-10 0 10 20 30 40 50
LUE
corr
ect
ion
fac
tor
Mean day temperature (oC)
Original
Calibrated
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0 0.5 1 1.5 2 2.5
Development stage
Stem
Leaves
Figure I.9 Original and calibrated relation between (a) mean day temperature (oC) and LUE correction factor and (b) CO2 concentration in the air (ppm) and LUE
correction factor and (c) calibrated relation between development stage and reallocation factor of assimilates from the stem and leaves to the grains of winter wheat
(Triticum aestivum L.).
a b
c
77
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
25-mrt 14-apr 4-mei 24-mei 13-jun 3-jul 23-jul 12-aug
Wat
er
con
ten
t m
3 m
-3
Date
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
25-mrt 14-apr 4-mei 24-mei 13-jun 3-jul 23-jul 12-aug
Wat
er
con
ten
t m
3 m
-3
Date
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
25-mrt 14-apr 4-mei 24-mei 13-jun 3-jul 23-jul 12-aug
Wat
er
con
ten
t m
3 m
-3
Date
Figure I.10 Critical water content for potential growth [red] and actual water
content [blue] of a winter wheat crop grown at The Bouwing [a], The Eest [b]
and PAGV [c], the Netherlands in the growing period of 1982/83.
a b
c
78
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
9-mrt 28-apr 17-jun 6-aug 25-sep
Wat
er
con
ten
t m
3 m
-3
Date
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
9-mrt 28-apr 17-jun 6-aug 25-sep
Wat
er
con
ten
t m
3 m
-3
Date
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
9-mrt 28-apr 17-jun 6-aug 25-sep
Wat
er
con
ten
t m
3 m
-3
Date
Figure I.11 Critical water content for potential growth [red] and actual water
content [blue] of a winter wheat crop grown at The Bowing [a], The Eest [b] and
PAGV [c], the Netherlands in the growing period of 1982/83.
a
c
b
79
0
20
40
60
80
100
120
140
160
180
19-mrt 8-apr 28-apr 18-mei 7-jun 27-jun 17-jul 6-aug 26-aug
De
pth
(cm
) Date
0
20
40
60
80
100
120
140
160
180
19-mrt 8-apr 28-apr 18-mei 7-jun 27-jun 17-jul 6-aug 26-aug 15-sep
De
pth
(cm
)
Date
0
20
40
60
80
100
120
140
160
180
19-jan 9-mrt 28-apr 17-jun 6-aug
De
pth
(cm
)
Date
Figure I.12 Soil water depth [blue] and rooting depth [green] of a winter wheat
crop grown in The Bouwing [a], The Eest [b] and PAGV [c], the Netherlands in
the growing period of 1982/83.
a b
c
80
R² = 0.36; ∆y = -0.342x
R² = 0.12; ∆y = -0.408x 15
25
35
45
55
1980 1985 1990 1995 2000 2005 2010 2015
De
viat
ion
fro
m l
on
gest
day
(d
ays)
Year
Normal sowing Late - Sown
*
a
b
0
0
c
a
0
R² = 0.26; ∆y = -0.346x
0
50
100
150
200
250
300
1980 1985 1990 1995 2000 2005 2010 2015
Tim
e (
day
s)
Year
Normal sown - Anthesis to harvest Late sown - anthesis to harvest
Normal sown - Emergence to Anthesis Late sown - emergence to anthesis
*
0
Figure I.13 . Simulated period from emergence to anthesis and anthesis to harvest [a] and deviation
between the longest day (21 June) and the median day of the period from anthesis to harvest [b] of normal
(10 October) and late (25 November) sown winter wheat, based on average data for radiation and annual
average CO2 levels over the period of 1981 to 2010 and actual minimum and maximum temperatures, using
the LINTUL model. In the equations of the relationships x is in years with x = year - 1981. ∆ stands for the
slope of a trend. Data series marked with an * do not show a significant (P < 0.05) trend.
b
*
a
b
a
0
0
81
0.30
0.35
0.40
0.45
0.50
0.55
0.60
1980 1985 1990 1995 2000 2005 2010 2015H
arve
st I
nd
ex
Year
Normal sowing - V1 Late sowing - V1
Normal sowing - V2 Late sowing - V2
10
12
14
16
18
20
22
24
26
1980 1985 1990 1995 2000 2005 2010 2015Ab
ove
gro
un
d b
iom
ass
(Mg
DM
ha
-1)
Year
Anthesis - V1 Harvest - V1
Anthesis - V2 Harvest - V2
8
9
10
11
12
13
14
15
16
1980 1985 1990 1995 2000 2005 2010 2015
Gra
in (
Mg
ha-1
)
Year
Normal sowing - V1 Late sowing - V1Normal sowing - V2 Late sowing - V2
10
12
14
16
18
20
22
24
26
1980 1985 1990 1995 2000 2005 2010 2015
Ab
ove
gro
un
d b
iom
ass
(Mg
DM
ha
-1)
Year
Anthesis - V1 Harvest - V1
Anthesis - V2 Harvest - V2
Figure I.14 Simulated aboveground biomass at anthesis and harvest of normal (10
October) [a] and late (25 November) [b] sown winter wheat, based on average data for radiation and annual average CO2
levels over the period of 1981 to 2010 and
actual minimum and maximum temperatures, using a LINTUL model calibrated for wheat varieties in the early 1980’s (V1) and a model adjusted to varieties around 2010 (V2. Additional information can be found in Table I.4
a a
b b
0
0
0 0.00
Figure I.15 Simulated grain yield (15% moisture) [c] and harvest index [d] of
normal (10 October) [a] and late (25 November) [b] sown winter wheat, based on average data for radiation and annual average CO2
levels over the period of 1981 to
2010 and actual minimum and maximum temperatures, using a LINTUL model calibrated for wheat varieties in the early 1980’s (V1) and a model adjusted to varieties around 2010 (V2. Additional information can be found in Table I.4
82
Table I.4 Equations, probability of the relation and F probability of changing from a linear to a quadratic equation of trends in simulated winter wheat (Triticum aestivum L.) biomass production and length of growing period for two sowing times and two sets of crop parameters based on two ‘varieties’, using average data over the period of 1980 to 2010 for radiation and annual average CO2
levels; and actual minimum and maximum temperatures for
simulations. ‘Variety’ 1 is the normal calibrated model, while ‘variety’ 2 has an adjusted harvest index (HI) to represent properties of varieties cultivated around 2010.
Dependent variable (Y)
Sowing date
Variety a
Relation to Y
Frelation
Fchange a
R2
adj.
Aboveground biomass at anthesis (Mg dm ha-1)
284 1 - 0.763 0.325 - 2 - 0.753 0.329 - 330 1 11.73 + 0.237x - 0.00733x2 0.091 0.045 0.10 2 12.58 + 0.248x - 0.00769x2 0.092 0.044 0.10
Aboveground biomass at harvest (Mg dm ha-1)
284 1 - 0.721 0.131 - 2 - 0.708 0.132 - 330 1 16.89 + 0.290x - 0.00896x2 0.075 0.038 0.11 2 18.14 + 0.298x - 0.00926x2 0.079 0.038 0.11
Grain 15% moisture (Mg ha-1)
284 1 - 0.365 0.194 - 2 - 0.271 0.194 0.01 330 1 - 0.829 0.146 - 2 - 0.985 0.147 -
Harvest index 284
1 - 0.471 0.860 -
2 - 0.332 0.831 - 330
1 - 0.211 0.264 0.02
2 - 0.133 0.246 0.05 Average LUE b correction factor temperature before anthesis
284 1 0.6109 + 0.00213x 0.058 0.196 0.09 2 0.6109 + 0.00213x 0.058 0.196 0.09 330 1 0.6561 + 0.00427 0.010 0.941 0.19 2 0.6561 + 0.00427 0.010 0.941 0.19
Average LUE b correction factor temperature after anthesis
284 1 - 0.026d 0.072 0.14 2 - 0.026d 0.072 0.14 330 1 - 0.159 0.630 0.04 2 - 0.159 0.630 0.04
Time of emergence (day of year)
284 1 - 0.706 0.610 - 2 - 0.706 0.610 - 330 1 - 0.375 0.235 - 2 - 0.375 0.235 -
Time of anthesis (day of year + 365)
284 1 551 - 0.374x 0.001 0.658 0.30 2 551 - 0.374x 0.001 0.658 0.30 330 1 552 - 0.317x 0.057 0.864 0.09 2 552 - 0.317x 0.057 0.864 0.09
Harvest time (day of year + 365)
284 1 589 - 0.483x <0.001 0.950 0.39 2 589 - 0.483x <0.001 0.950 0.39 330 1 591 - 0.407x 0.020 0.957 0.15 2 591 - 0.407x 0.020 0.957 0.15
Time from emergence to anthesis (d)
284 1 250 - 0.346x 0.002 0.747 0.26 2 250 - 0.346x 0.002 0.747 0.26 330 1 185 - 0.750x 0.060 0.153 0.09
83
2 185 - 0.750x 0.060 0.153 0.09 Time from anthesis to harvest (d)
284 1 - 0.102 0.415 0.01 2 - 0.102 0.415 0.01 330 1 - 0.199 0.787 0.02 2 - 0.199 0.787 0.02
Cumulative radiation from emergence to anthesis (kJ m-2)
284 1 2160 - 6.63x 0.002 0.760 0.27 2 2160 - 6.63x 0.002 0.760 0.27 330 1 2016 - 7.06x <0.001 0.856 0.32 2 2016 - 7.06x <0.001 0.856 0.32
Cumulative radiation from anthesis to harvest (kJ m-2)
284 1 - 0.274 0.330 0.01 2 - 0.274 0.330 0.01 330 1 - 0.349 0.832 - 2 - 0.349 0.832 -
Deviation of grain filling period from longest day (d)
284 1 33 - 0.408 <0.001 0.743 0.36 2 33 - 0.408 <0.001 0.743 0.36 330 1 34 - 0.342 0.036 0.842 0.12 2 34 - 0.342 0.036 0.842 0.12
a Representing the added significance when going from a linear to a quadratic relation.
b LUE is light use efficiency.
c Average of minimum and maximum daily temperature
d Residuals were not random for this relation.
84
Table I.5 Equations, F probability of the relation and F probability of changing from a linear to a quadratic equation of trends in simulated winter wheat (Triticum aestivum L.) biomass production and length of growing period for two sowing times and two sets of crop parameters based on two ‘varieties’, using average data over the period of 1980 to 2010 for minimum and maximum temperatures and annual average CO2
levels; and actual radiation for
simulations. ‘Variety’ 1 is the normal calibrated model, while ‘variety’ 2 has an adjusted harvest index (HI) to represent properties of varieties cultivated around 2010.
Dependent variable (Y)
Sowing date
Model a
Relation to Y
Frelation
Fchangeb
R2
adjusted
Aboveground biomass at anthesis (Mg dm m-2)
284 1 13.87 + 0.129x < 0.001 0.498 0.46 2 14.80 + 0.137x < 0.001 0.498 0.46 330 1 10.73 + 0.103x < 0.001 0.314 0.32 2 11.51 + 0.109x < 0.001 0.319 0.32
Aboveground biomass at harvest (Mg dm m-2)
284 1 19.15 + 0.151x < 0.001 0.605 0.44 2 20.41 + 0.160x < 0.001 0.606 0.44 330 1 15.68 + 0.128x < 0.001 0.370 0.32 2 16.83 + 0.134x < 0.001 0.387 0.32
Grainc (Mg m-2)
284 1 10.06 + 0.0659x 0.005 0.907 0.22 2 12.27 + 0.0829x 0.002 0.855 0.27 330 1 9.113 + 0.0688x 0.004 0.648 0.23 2 11.05 + 0.0821x 0.003 0.631 0.25
Harvest index 284 1 - 0.325 0.716 -
2 - 0.318 0.705 - 330 1 - 0.576 0.620 - 2 - 0.528 0.554 -
Cumulative radiation from emergence to anthesis (kJ m-2)
284 1 1899 + 12.3x <0.001 0.195 0.49 2 1899 + 12.3x <0.001 0.195 0.49 330 1 1736 + 11.9x <0.001 0.263 0.48 2 1736 + 11.9x <0.001 0.263 0.48
Cumulative radiation from anthesis to harvest (kJ m-2)
284 1 620.7 + 2.83x 0.062 0.479 0.09 2 620.7 + 2.83x 0.062 0.479 0.09 330 1 620.7 + 2.83x 0.062 0.479 0.09 2 620.7 + 2.83x 0.062 0.479 0.09
a Model 1 is the normal calibrated model, while model 2 has an adjusted harvest index (HI) to represent
properties of varieties cultivated around 2010. b Representing the added significance when going from a linear to a quadratic relation.
c 15% moisture
85
Table I.6 Equations, F probability of the relation and F probability of changing from a linear to a quadratic equation of trends in simulated winter wheat (Triticum aestivum L.) biomass production and length of growing period for two sowing times and two sets of crop parameters based on two ‘varieties’, using average data over the period of 1980 to 2010 for minimum and maximum temperatures and radiation; and actual annual average CO2
levels for simulations. ‘Variety’ 1 is the
normal calibrated model, while ‘variety’ 2 has an adjusted harvest index (HI) to represent properties of varieties cultivated around 2010.
Dependent variable (Y)
Sowing date
Model a Relation to Y Frelation Fchange b R2
adjusted
Aboveground biomass at anthesis (g dm m-2)
284 1 14.57 + 0.100x - 0.00155x2 < 0.001c < 0.001 1.00 2 15.55 + 0.106x - 0.00164x2 < 0.001c < 0.001 1.00 330 1 11.27 + 0.0829x - 0.00128x2 < 0.001c < 0.001 1.00 2 12.08 + 0.0876x - 0.00135x2 < 0.001c < 0.001 1.00
Aboveground biomass at harvest (g dm m-2)
284 1 19.73 + 0.134x - 0.00207x2 < 0.001c < 0.001 1.00 2 21.03 + 0.142x - 0.00219x2 < 0.001c < 0.001 1.00 330 1 16.16 + 0.118x - 0.00183x2 < 0.001c < 0.001 1.00 2 17.31 + 0.125x - 0.00193x2 < 0.001c < 0.001 1.00
Grain (g dm m-2)
284 1 10.23 + 0.0621x - 0.000962x2 < 0.001c < 0.001 1.00 2 12.51 + 0.0767x - 0.00119x2 < 0.001c < 0.001 1.00 330 1 9.344 + 0.0643x - 0.000999x2 < 0.001c < 0.001 1.00 2 11.31 + 0.0771x - 0.00120x2 < 0.001c < 0.001 1.00
Harvest index 284 1 0.441 - 2.97*10-4x - 4.82*10-6x2 < 0.001c < 0.001 1.00
2 0.506 - 2.83*10-4x - 4.54*10-6x2 < 0.001c < 0.001 1.00 330 1 0.491 - 2.01*10-4x - 3.10*10-6x2 < 0.001c < 0.001 1.00 2 0.556 - 2.09*10-4x - 3.29*10-6x2 < 0.001c < 0.001 1.00
Correction factor of LUE for air CO2 concentration
284 1 0.937 + 5.5*10-3x - 8.55*10-5x2 < 0.001c < 0.001 1.00 2 0.937 + 5.5*10-3x - 8.55*10-5x2 < 0.001c < 0.001 1.00 330 1 0.937 + 5.5*10-3x - 8.55*10-5x2 < 0.001c < 0.001 1.00 2 0.937 + 5.5*10-3x - 8.55*10-5x2 < 0.001c < 0.001 1.00
a Model 1 is the normal calibrated model, while model 2 has an adjusted harvest index (HI) and LUE to represent properties
of varieties cultivated around 2010. b Representing the added significance when going from a linear to a quadratic relation.
c Residuals were not random for this relation.
86
∆y = 9.83x - 0.269x2
R2 = 0.27
0
500
1000
1500
2000
2500
1980 1985 1990 1995 2000 2005 2010 2015
Cu
mm
ula
tive
rad
iati
on
((k
J m
-2 )
Year
Anthesis untill harvest Emergence untill anthesis
*
0
500
1000
1500
2000
2500
1980 1985 1990 1995 2000 2005 2010 2015
Cu
mm
ula
tive
rad
iati
on
((k
J m
-2 )
Year
Anthesis untill harvest Emergence untill anthesis
*
*
Figure I.16 Cumulative radiation during simulated crop stages of normal (10 October) [a]
and late ( 25 November) [b] sown winter wheat crops in the Netherlands, based on real
weather data. Equations of the relationships are described in table I.7. Data series marked
with an * do not show a significant trend.
a
b
87
Table I.7. Equations, F probability of the relation and F probability of changing from a linear to a quadratic equation of trends in simulated winter wheat (Triticum aestivum L.) biomass production and length of growing period for two sowing times and two sets of crop parameters based on two ‘varieties’, using actual weather data and annual average CO2
levels over the period of 1980 to 2010. ‘Variety’ 1 is the normal calibrated model, while ‘variety’ 2 has an
adjusted harvest index (HI) to represent properties of varieties cultivated around 2010.
Dependent variable (Y)
Sowing date
Model a Relation to Y Frelation Fchange b R2
adjusted
Aboveground biomass at anthesis (g dm m-2)
284 1 13.38 + 0.163x <0.001 0.115 0.52 2 14.28 + 0.171x <0.001 0.115 0.52 330 1 9.236 + 0.467x - 0.0102x2 <0.001 0.028 0.50 2 9.944 + 0.492x - 0.0107x2 <0.001 0.028 0.50
Aboveground biomass at harvest (g dm m-2)
284 1 18.32 + 0.211x <0.001 0.082 0.58 2 19.54 + 0.222x <0.001 0.082 0.58 330 1 13.52 + 0.583x - 0.123x2 <0.001 0.023 0.56 2 14.60 + 0.605x - 0.127x2 <0.001 0.024 0.56
Grain (g dm m-2)
284 1 9.207 + 0.204x - 0.00412x2 <0.001 0.022 0.61 2 11.26 + 0.250x - 0.00508x2 <0.001 0.015 0.64 330 1 7.974 + 0.278x - 0.00606x2 <0.001 0.005 0.64 2 9.79 + 0.324x - 0.00703x2 <0.001 0.007 0.63
Harvest index 284 1 0.453 - 9.98*10-4x 0.029 0.990 0.13
2 0.519 - 1.13*10-3x 0.013 0.941 0.17 330 1 0.495 - 1.32*10-3x 0.021 0.443 0.15 2 0.562 - 1.50*10-3x 0.019 0.355 0.20
Cumulative radiation from emergence to anthesis (kJ m-2)
284 1 1989 + 5.16x 0.055 0.363 0.09 2 1989 + 5.16x 0.055 0.363 0.09 330 1 1845 + 4.93x 0.061 0.246 0.09 2 1845 + 4.93x 0.061 0.246 0.09
Cumulative radiation from anthesis to harvest (kJ m-2)
284 1 590.0 + 9.83x - 0.269x2 0.005 0.017 0.27 2 590.0 + 9.83x - 0.269x2 0.005 0.017 0.27 330 1 621.8 + 1.76x 0.082 0.126 0.07 2 621.8 + 1.76x 0.082 0.126 0.07
a Model 1 is the normal calibrated model, while model 2 has an adjusted harvest index (HI) and LUE to represent
properties of varieties cultivated around 2010. b Representing the added significance when going from a linear to a quadratic relation.
c LUE is light use efficiency
d Average of minimum and maximum daily temperature
88
Appendix II
Table II.1 Observed development stages of winter wheat (Triticum aestivum L.) sown from September to January in five different years.
Year Sowing date Emergence
(F1)* Start grain filling
(F10.5.4) Maturity
(F11.3)
1979
21-9-1978 30-9-1978 5-7-1979 12-8-1979
1979 16-10-1978 31-10-1978 11-7-1979 20-8-1979
1979 15-11-1978 22-12-1978 11-7-1979 29-8-1979
1979 15-12-1978 3-4-1979 26-7-1979 5-9-1979
1980 17-10-1979 8-11-1979 26-6-1980 12-8-1980
1980 1-11-1979 29-11-1979 28-6-1980 13-8-1980
1980 15-11-1979 8-12-1979 30-6-1980 15-8-1980
1980 17-12-1979 25-2-1980 7-7-1980 16-8-1980
1982 16-10-1981 2-11-1981 1-7-1982 4-8-1982
1982 4-11-1981 30-11-1981 1-7-1982 5-8-1982
1982 18-11-1981 29-12-1981 4-7-1982 6-8-1982
1982 22-1-1982 12-3-1982 8-7-1982 8-8-1982
1983 15-10-1982 26-10-1982 29-6-1983 30-7-1983
1983 1-11-1982 19-11-1982 4-7-1983 1-8-1983
1983 15-11-1982 18-12-1982 6-7-1983 3-8-1983
1983 16-12-1982 19-1-1983 8-7-1983 6-8-1983
1983 17-1-1983 7-3-1983 12-7-1983 9-8-1983
1984 14-10-1983 31-10-1983 6-7-1984 18-8-1984
1984 1-11-1983 25-11-1983 11-7-1984 20-8-1984
1984 16-11-1983 26-12-1983 16-7-1984 23-8-1984
1984 20-12-1983 16-1-1984 18-7-1984 25-8-1984
1984 16-1-1984 14-3-1984 25-7-1984 29-8-1984
* Development stage according to Feekes scale of cereal development.
89
Appendix III Table III.1 Observed yields of different winter wheat (Triticum aestivum L.) varieties sown at different locations and sowing densities in the Netherlands from 1976 to 1998.
Source
Location
Sowing date
Sowing densitya
(seeds m-2)
Plant densityb
(plants m-2)
Initial weight leaves (g m-2)
Initial weight stem (g m-2)
Aboveground biomass (kg ha-1)
Grain yield (kg ha-1)
Variety
(Darwinkel, 1994) Ebelsheerd 24-10-1990 375 290 0.10 0.06 1,581 751 Herzog, Obelisk and Urbanc
(Darwinkel, 1994) Westmaas 26-10-1990 325 168 0.06 0.03 1,726 832 Herzog, Obelisk and Urban
(Darwinkel, 1994) Wijnandsrande 26-10-1990 300 178 0.06 0.03 1,496 723 Herzog, Obelisk and Urban
(Darwinkel, 1985) PAGVd 19-10-1976 413 280 0.10 0.05
705.5 Caribo
(Darwinkel, 1985) PAGV 22-9-1978 425 319 0.11 0.06
774.4 Caribo (Darwinkel, 1985)
PAGV 6-10-1978 425 319 0.11 0.06
694.5 Caribo (Darwinkel, 1985)
PAGV 24-10-1978 425 319 0.11 0.06
612.9 Caribo (Darwinkel, 1985)
Bemmelenhoeve 3-10-1978 350 263 0.09 0.05
791.4 Donata (Darwinkel, 1985)
Bemmelenhoeve 30-10-1978 350 263 0.09 0.05
781.2 Donata (Darwinkel, 1985)
PAGV 20-9-1979 338 170 0.06 0.03
822.0 Arminda (Darwinkel, 1985)
PAGV 22-10-1979 338 200 0.07 0.04
761.6 Arminda (Darwinkel, 1985)
Westmaas 20-9-1979 500 310 0.11 0.06
827.1 Okapi (Darwinkel, 1985)
Westmaas 17-10-1979 500 310 0.11 0.06
824.5 Okapi (Darwinkel, 1985)
PAGV 14-10-1980 150 135 0.05 0.03
772.7 Arminda (Darwinkel, 1985)
PAGV 15-10-1980 300 270 0.10 0.05
775.2 Arminda
90
(Darwinkel, 1985) PAGV 16-10-1980 450 390 0.14 0.08
742.1 Arminda
(Darwinkel, 1985) PAGV 17-10-1980 600 520 0.19 0.10
763.3 Arminda
(Darwinkel, 1985) Feddemaheerd 17-9-1980 625 469 0.17 0.09
907.0 Okapi
(Darwinkel, 1985) Feddemaheerd 13-10-1980 625 469 0.17 0.09
774.4 Okapi
(Darwinkel, 1985) Bemmelenhoeve 1-10-1980 620 465 0.17 0.09
901.0 Okapi
(Darwinkel, 1985) Westmaas 25-9-1980 625 480 0.17 0.09
871.3 Arminda
(Darwinkel, 1985) Westmaas 22-10-1980 625 480 0.17 0.09
832.2 Arminda
(Darwinkel, 1985) Rusthoeve 7-11-1980 375 306 0.11 0.06
790.5 Arminda
(Darwinkel, 1985) Rusthoeve 7-11-1980 625 527 0.19 0.10
796.5 Arminda PPO praktijkonderzoek PAV PPOd 17-10-1996 350 260 0.09 0.05 1,207.7
Vivant
PPO praktijkonderzoek PAV PPO 17-10-1996 350 260 0.09 0.05 1,973.3 1015.6 Vivant PPO praktijkonderzoek PAV PPO 11-1-1999 400 260 0.09 0.05 1,039.7
Ritmo
PPO praktijkonderzoek PAV PPO 11-1-1999 400 260 0.09 0.05 1,962.9 963.5 Ritmo PPO praktijkonderzoek PAV PPO 6-10-1998 350 260 0.09 0.05 1,305.5
Ritmo
PPO praktijkonderzoek PAV PPO 6-10-1998 350 260 0.09 0.05 2,238.3 1115.3 Ritmo
a Red numbers are estimated based on seed weight per m
-2.
b Green numbers are estimated based on the sowing density.
c Average of three varieties.
d PAGV has become PAV PPO.
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