DEPARTMENT for ENVIRONMENT, FOOD and RURAL AFFAIRS
Research and Development
CSG 15
Final Project Report
(Not to be used for LINK projects)
Two hard copies of this form should be returned to:
Research Policy and International Division, Final Reports Unit
DEFRA, Area 301
Cromwell House, Dean Stanley Street, London, SW1P 3JH.
An electronic version should be e-mailed to [email protected]
Project title
How far will medium term weather forecasts improve assessment of risks?
DEFRA project code
ES0101
Contractor organisation
and location
Rothamsted Research
Harpenden
Hertfordshire AL5 2JQ
Total DEFRA project costs
Project start date
£ 58,309
01/08/01
Project end date
31/07/02
Executive summary (maximum 2 sides A4)
The foresight exercise has suggested that there might be a value to the UK for medium-term weather
forecasts. Since much advice now being given to farmers is able to make use of weather data, DEFRA
commissioned Rothamsted Research to use it’s SUNDIAL modelling system to explore the potential value of
such weather forecasts to the farming industry.
Farming is under diverse pressures: on the one hand environmental norms must be met and adhered to, on
the other hand profitability within the industry is now lower than ever. Even though losses of N (leaching or
denitrification) increase rapidly with applications of N beyond the physiological optimum, farmers in the 1980’s
and to a lesser extent the 1990’s have tended to over apply N because the consequence of applying too little
is a large loss of yield (and profit) whereas a major consequence of applying too much is off-farm diffuse
pollution.
Systems such as SUNDIAL are intended to optimise N fertiliser advice for farmers by taking full account of the
N that can be supplied from soil, including mineralisation post-application. In this way, yields can be
maximised and nitrogen pollution minimised. Predictions from SUNDIAL, however, depend on the weather
forecast post-application and in general there is a risk in using SUNDIAL (or any other N prediction system)
that limits to achieving the potential yield, apparent at the fertiliser date, could be made up by harvest under
favourable conditions. Accordingly DEFRA commissioned RRes to investigate the potential benefits of
medium-term weather forecasts for arable farming in England and Wales.
There are two aspects to a medium-term weather forecast: how long into the future the weather is known and
with what accuracy that weather is known. Forecasts of 1, 3, 5, 7, 9 and 27 weeks duration and of 10, 30, 50,
70 and 90% accuracy were compared with no prior knowledge and complete prior knowledge (100% accurate
forecasts of 27 weeks duration). The effect during 80 separate seasons on the response to applications of N
optimised using SUNDIAL (with weather known up until the date of application) with each level of prior
knowledge of the weather subsequent to the application was tested. The change in leaching, denitrification
CSG 15 (9/01)
1
Project
title
How far will medium term weather forecasts improve
assessment of risks?
DEFRA
project code
ES0101
and N-uptake were calculated for each of the weather scenarios. This change in N-uptake can be expressed
as an equivalent change in yield so the changes in tonnes per hectare as well as the gross profit were
calculated for four major arable crops that encompass 85% of the arable industry in England and Wales.
In general the change in N losses by leaching or denitrification were not large but nor were the absolute values
of the losses themselves. The average change during 80 years was very close to zero and prior knowledge
was just as likely to lead to a small increase as a small decrease in losses. The benefits (reduced loss) of a
reasonable forecast of the weather were less than 1 kg N ha-1 in the case of denitrification and not more than 2
kg N ha-1 at most for leaching. This is against a background of a loss of 8 kg N ha-1 denitrification (baseline
loss) on average during the 80 years investigated. Baseline leaching losses were 20 kg N ha-1.
The maximum average benefit in terms of a change in N uptake (avoided loss) of medium-term weather
forecasts was just under 7 kg N ha-1. The difference in benefits of medium-term weather forecasts on Nuptake between regions of England and Wales were not great on average, nor were there large differences
between soil types, but wheat benefited more (a little more than 7 kg N ha-1 per year on average) than spring
barley (5 kg N ha-1) with oil-seed rape and potatoes benefiting at amounts in between.
Accurate forecasts of 27 weeks duration might help all farmers avoid an unnecessary shortfall in the uptake of
N of arable crops of more than 6 kg N ha-1 on average. In some years this could be more in some years less.
In 20% of years accurate forecasts of this duration would benefit farmers by the equivalent of half a tonne of
yield in avoided shortfall; in 4% of years by one tonne. With less information, a forecast of 3 weeks duration
that is 50% reliable, a benefit equivalent to half a tonne might be achieved 5% of the time, and a benefit of 1
tonne 1.5% of the time.
The distribution of change in loss was approximately symmetrical for both leaching and denitrification, and the
average loss was close to zero. The picture was different with crop N uptake, however, where in most years
increased prior knowledge increased the amount of N taken up by the crop and by implication the yield and
profit. There were very few years in which the prior knowledge decreased N-uptake; about 40% of the time
prior knowledge made little difference to crop N uptake. Increases took place in about 50% of the growing
seasons.
An accurate, 27 week forecast could benefit arable farming in England and Wales by more than £50-70M per
annum even though the amounts of nitrogen involved are small. Less accurate forecasts of only a few weeks’
duration could still have considerable commercial benefit. Currently, forecasts up to one week are available.
Reliable information even three weeks into the future could increase crop N uptake by 2 kg N ha-1 on average.
Translated nationally into profit to the industry this amount is equivalent to more than £20M annually.
In conclusion it is likely that medium-term forecasts would benefit the users of N fertiliser advisory systems
such as SUNDIAL greatly. SUNDIAL helps farmers to minimise losses to the environment while maintaining
yield, but it is inevitable that in some years weather will be very different from what is expected. Since the UK
has recently declared more than 60% of the land area to be nitrate sensitive, farmers in these areas face
restricted use of N fertiliser. The major constraint in these areas then becomes environmental. Under these
circumstances it will be difficult for farmers to apply insurance dressings of N and they will need a system like
SUNDIAL in order to optimise N application. Medium-term weather forecasts of good accuracy are likely to
become a priority for farmers in these regions and for all farmers relying closely on advisory systems for
predicting N fertiliser requirement. The benefit to the industry as a whole is of the order of tens of millions of
pounds each year in avoided loss of yield. This benefit must be weighed against the cost of developing and
delivering a medium-term forecast capability, however.
CSG 15 (9/01)
2
Project
title
How far will medium term weather forecasts improve
assessment of risks?
DEFRA
project code
ES0101
Scientific report (maximum 20 sides A4)
Farming is under diverse pressures, on the one hand environmental norms must be met and adhered to, on the
other hand profitability within the industry is now lower than ever. Nitrogen fertiliser applications are an
extremely difficult issue for farmers to balance between these two pressures. Nitrate leaching into ground and
surface water must be kept below tight limits in order to comply with EU water quality legislation. Losses of
nitrous oxide (N2O) through denitrification have serious consequences for global warming. However, most
crops respond readily to nitrogen and it is a relatively cheap input. Even though losses of N (leaching or
denitrification) increase rapidly with applications of N beyond the physiological optimum, farmers in the
1980’s and to a lesser extent the 1990’s have tended to over apply N because the consequence of applying too
little is a large loss of yield (and profit) whereas a major consequence of applying too much is off-farm diffuse
pollution.
Systems such as SUNDIAL are intended to optimise N fertiliser advice for farmers by taking full account of
the N that can be supplied from soil, including mineralisation post-application. In this way, yields can be
maximised and nitrogen pollution minimised. Predictions from SUNDIAL, however, are only as good as the
estimates of the weather post application and in general there is a risk in using SUNDIAL (or any other N
prediction system) that differences from the potential yield, apparent at the fertiliser date, could be made up by
harvest under favourable conditions. To always apply N with maximum yield in mind will lead to losses in
many years. To apply N with a view to minimising environmental losses will lead to a shortfall in yield and
profit. It is with this standpoint in mind that RRes examined the benefits of medium-term weather forecasts on
reducing nitrogen pollution and on reducing losses of yield.
There is potential for medium-term weather forecasts to be made available to the farming community and it
seems likely that such information might improve the accuracy with which farmers can obtain site and seasonspecific fertiliser recommendations using models such as SUNDIAL. The Foresight exercise has suggested the
value of medium-term weather forecasts (2-3 month, Anon, 2001) and so DEFRA commissioned Rothamsted
Research to examine the potential benefits to the farming community of having access to reliable prior
knowledge of weather of medium-term duration. The purpose of the proposed project was to quantify the
potential of medium-term forecasts to improve the accuracy of nitrogen fertiliser recommendations and
assessment of the risks of loss to the environment and reduced crop nitrogen uptake. By quantifying the extent
of the potential benefits we hope that DEFRA will be able to assess the emphasis that should be given to
medium-term forecasts in other funding
The scientific questions addressed were as follows:
1. Can medium term forecasts improve accuracy of nitrogen fertiliser recommendations? Simulations
were performed assuming (1) no access to forecasts of future weather at the time when a fertiliser
recommendation is needed (weather obtained, as is currently practised in SUNDIAL-FRS, using the
weather generator funded under (NT2307), (2) access to weather forecasts of great accuracy onwards from
the time that a fertiliser application is made (hypothetical weather forecasts obtained using the weather
generator developed under NT2307), (3) access to weather forecasts with reduced accuracy.
2. Can medium term forecasts improve assessment of risks of loss to the environment and reduced crop
nitrogen uptake? Risks of loss to the environment and reduced crop nitrogen uptake have been quantified
for the range of crops, soils and weather conditions assuming (1) no access to weather forecasts (2) access
to accurate weather forecasts, and (3) access to weather forecasts with reduced accuracy. The results are
expressed in terms of kg N ha-1 lost and value of yield lost.
3. What accuracy in medium term weather forecasts is needed to improve recommendations? The
accuracy of forecasts was adjusted by introducing random variation sampled from a known distribution.
CSG 15 (9/01)
3
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
The improvement in fertiliser recommendation and risk assessment with improvement of accuracy of the
forecast were quantified for a range of crops, soils and weather conditions.
The questions posed here were answered using SUNDIAL (Smith et al., 1996). This system suggests an
amount of fertiliser to be applied based on known weather up until the date at which fertiliser must be applied
and on expected weather in the period between application and harvest. In general SUNDIAL has been shown
to agree well with RB209 (MAFF, 2000), the current DEFRA-sponsored recommendations for N (NT2306).
Because SUNDIAL takes account of expected weather post-application it is able to recommend lower
applications in general and this is of great environmental benefit. The analysis carried out in this project
reflects the increase in yield or reduction in loss of N (denitrification or leaching) that can come about with
increasing prior knowledge of weather if SUNDIAL is used to provide fertiliser advice.
The objectives of the project were achieved as follows:
01. Construction of hypothetical weather data sets made up of two periods: up to the date of the application of
fertiliser and after application for all combinations of dry to wet and cool to warm weather at different times
of the year. Decisions about nitrogen fertiliser applications to cereals and oilseeds are usually finalised by
February of the current growing season. Generated weather data used to run the model up to this point was
the same for all simulations. The weather required to run the model predictively were obtained in 3 ways:
(1) simulations assuming no access to weather forecasts was used weather files produced by the our weather
generator (NT2307), (2) simulations assuming access to weather forecasts with 100% accuracy using a
statistical sample of 80 weather files of historical weather data, (3) simulations assuming access to weather
forecasts with reduced accuracy used a statistical sample of 80 weather files with the required level of
variation introduced relative to (2). The forecasts were projected into the future over a range of durations (1,
3, 5, 7, 9 and 27 weeks) after application of fertiliser.
02. Construction of SUNDIAL set-up files on sand, loam and clay soils for different combinations of winter
cereals, spring cereals, leaky crops and high residue crops. SUNDIAL set-up files were constructed
including different combinations of winter cereals (winter wheat), spring cereals (spring barley), a leaky
crop (potatoes) and a high residue input crop (winter oilseed rape). For consistency, winter wheat was taken
as the previous crop in all cases.
03. Running SUNDIAL simulations for all combinations of soils, crops and weather conditions assuming no
access to weather forecasts and access to weather forecasts with complete and reduced accuracy. The
SUNDIAL model was run for all combinations of soils, crops and weather conditions established in
objectives 01 and 02.
04. Investigating the changes in variability as a result of having prior knowledge of weather associated with
changes in crop nitrogen uptake, leaching losses, gaseous emissions, and possible increase in crop yield.
The results of simulations obtained using (1) no forecasts (2) forecasts with 100% accuracy and (3)
forecasts with reduced accuracy were compared. The results are expressed both in amounts of nitrogen and
in terms of the potential economic saving. Variability is an important aspect of the project, as increased
variability in weather can greatly increase uncertainty in nitrogen losses from the soil /crop system. Under
variable conditions, medium term forecasts are likely to be of much greater economic value than in more
constant environments.
4
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
METHODOLOGY
SUNDIAL
DEFRA have supported the construction of the SUNDIAL Fertiliser Recommendation system (SUNDIALFRS, Smith et al., 1996). This system uses a computer simulation model (Bradbury et al., 1993) of N turnover
in soil to estimate the supply and loss of N to a number of arable crops. SUNDIAL simulates the effect of
weather on N mineralisation, crop uptake and loss up to the date at which fertiliser is to be applied. Then using
expected weather (from historical records) the system optimises the amount of fertiliser that should be applied
for the expected conditions with a user-supplied maximum potential yield. SUNDIAL keeps track of leaching
and denitrification and returns a value for nitrogen uptake by the crop. Simulations were carried out until
harvest using differenr levels of prior knowledge of the weather (see next section). In general simulations using
reduced prior knowledge of the weather gave lower yields than where complete knowledge of the weather was
available . SUNDIAL does not predict yield, but if the potential uptake is reduced for any reason yield is
reduced. This loss of yield, leaching and denitrification are the yardsticks by which we assess the value of
access to medium-term weather forecasts.
The Weather Generator
A weather generator has been constructed during the DEFRA-funded project NT2307 and used in the current
project to produce series of weekly values for rain, evapotranspiration (ET) and the weekly mean of daily mean
temperature (Tmean). These are the weather data required by SUNDIAL. The generator is controlled by
characteristic parameters that describe the weather for a particular location, such that the weather data generated
is statistically similar to observed weather at that location. For details of the regions see the Regions section
below and Table 1.
Two sets of weather data were generated. The first series is expected weather, which is the weather used to
optimise SUNDIAL at the time that a decision on fertiliser application needs to be made and represents the
forecast at that time. The second series, which deviates from the first, is realized weather and represents the
weather that actually occurred. These terms, Expected and Realized weather will be used throughout this report.
The extent of the deviation of the second (realized) series from the first (expected) is specified by the “Forecast
Accuracy Parameter” (Pfa) and has a value between 0 and 1. A value of 1 gives no deviation of realized
weather from the expected representing a “perfect” forecast, a lower value of Pfa signifies a forecast of less
accuracy. By increasing Pfa over the range 0-1, prior knowledge of the weather is increased from 0 to 100%.
Reliability of prior knowledge is one issue, the other is the duration of that prior knowledge. We have
examined the effect of having prior knowledge of a given accuracy (Pfa 0-1) for durations of 1, 3, 5, 7, 9 and 27
weeks.
Generation of rainfall
The weather generator parameters for a particular location contain a probability distribution function (PDF) of
weekly rain amounts for each of the 13 four-week periods of the year. A rain amount is generated by first
producing a random number (a uniform deviate in the range 0 to 1), and transforming this using the PDF. A
series of rain amounts is therefore the result of transforming a random series using the PDF. Without further
modification the transformed series would be expected rain.
5
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
To produce realized rain, the same random series generated for the expected rain is supplemented by an
independent random component before transforming to rain amounts. The magnitude of this independent
random component is set by Pfa.
The random series, Rr, used for realized rain is given by:
Rr = Re + Ri * (1-Pfa)
where Re is the random series used for expected rain and Ri is an independent random series. The modified
random series is constrained to the range 0 to 1 as follows:
Rr  2  Rr ; Rr  1,
Rr  0  Rr ; Rr  0
Persistence is implemented as a first order Markov chain (Gabriel and Neumann, 1964). A uniformly
distributed random number is generated; if the random number is below 0.57 (the calculated probability across
the UK that two consecutive weeks are both dry or both wet) the rainfall is generated on the half of the
distribution to which the preceding week’s rainfall belongs, otherwise on the other half. The resultant random
numbers are transformed to rain amounts using the PDF of rainfall at the weather station of interest.
Generation of ET and Tmean
The long-term weekly averages and standard deviations of ET and Tmean are also generated from locationspecific parameters.
To generate a weekly value of either ET or Tmean, a weekly residual of the variate is randomly generated, then
multiplied by the standard deviation for the week to give the difference from the weekly mean; this is added to
the weekly mean to give the generated value. The process that generates the weekly series of residuals for ET
and Tmean includes a description of the correlation between, and the persistence of, temperature and
evaporation. Thus there is an increased probability that a warmer than average week will be followed by
another warm week, and that it will coincide with above average evapotranspiration.
Weather data representing the forecast is first generated using a series of residuals as described above. To
generate the realized weather representing the outcome of that forecast, a new series of residuals is generated
that tracks the original series, but deviates from it by a random amount with an average magnitude dependent
on Pfa. The realized ET and Tmean are calculated from the new series of residuals in the way previously
described under rainfall.
Zero-accuracy forecasts
To produce pairs of weather data series that represent a zero-accuracy forecast, the second set of weather is
generated in the same way as the expected weather, but independently of the first set; since both are generated
stochastically, they are randomly related, i.e. Pfa=0.
The relationship between Pfa and forecast accuracy.
Pfa has a range between zero for the totally random forecast, and one for the perfect forecast. There is not an
explicit relationship between Pfa and any measure of forecast accuracy, so the relationship with standard
6
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
measures of forecast accuracy was found empirically. An explanation of the work undertaken to confirm that
Pfa gave a reasonable representation of forecast accuracy is given in an appendix to this report.
Table 1: Weather stations used in the simulations
Site
Long Ashton
Starcross
Carlisle
Bala
Rothamsted
Woburn
East Malling
Cambridge
Cawood.
Leeming
Rainfall (annual mm)
Wet
892
Wet
810
Wet
811
Wet
1257
Moderate
671
Moderate
640
Dry
614
Dry
553
Dry
587
Dry
607
Temperature (mean annual oC)
Warm
10.1
Warm
10.23
Cool
8.97
Cool
8.55
Moderate
9.19
Moderate
9.41
Warm
9.96
Warm
9.99
Cool
9.18
Cool
9.08
Location
ST 537699
SX 972821
SH 935356
NY 384603
TL 132134
SP 964360
TQ 708570
TL 434606
SE 561366
SE 305890
Quality of generated data
The generated data was tested for bias, persistence of rain amount and reproduction of variance. There was no
bias between different forecasts, but there was a decrease in persistence of rain amount. These results are also
presented in the appendix.
Soil types
Three soil types were used in this project: sand (leaky), loam and clay (retentive), these soil types are the
default soils used by the SUNDIAL model.
Regions
Ten weather stations were chosen for the simulations representing wet, moderate rainfall and dry regions and
warm, moderate or cool conditions (Table 1)
Cropping
Four crops were investigated to represent the major classes of arable crops grown in England and Wales: a
winter cereal, winter wheat; a spring sown cereal, spring barley; a high residue crop, winter oil seed rape and a
leaky (root) crop, potatoes. These crops cover 85% of the land in arable cropping in England and Wales
(Appendix Table 2). Winter wheat was the previous crop in all cases.
The potential yields and dates of the crops used in the simulations are shown in Table 2. Winter oilseed rape
always had a 30 kg dressing of fertiliser nitrogen at drilling. The residues of the potato crop were incorporated,
those of the other crops were removed. For all simulations, the crop preceding the crop of interest was winter
wheat. The total applications of fertiliser nitrogen to the preceding crop were 160kg for sand, 220kg for loam
and for clay in wet or moderate locations, and 180 kg for clays in dry locations. These recommendations were
taken from RB209 (MAFF, 2000).
7
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
Table 2: Potential yield (tonnes ha-1) and dates of establishment and harvest of the crops used in the
simulations.
Potential yield
Crop
Sand
Loam or clay
Winter wheat 8.0
9.0
Spring barley 5.5
6.0
Winter OSR 3.5
3.5
Main potatoes 42.5
42.5
Dates
Establish
8 Oct
18 Feb
3 Sep
1 Apr
Harvest
12 Aug
12 Aug
5 Aug
30 Sep
Sequences of weather data
Within the context of this work, there is perfect knowledge of the past weather, for the duration of the forecast
(the forecast period), the weather is known with a specified level of accuracy, and following the forecast period
there is no prior knowledge of the weather. Paired sets of forecast and realized weather data were constructed
using generated weather for a particular site. For the period prior to fertiliser application, which represents the
past, forecast and realized weather data were identical. From fertiliser application to the end of the simulation,
forecast weather was the continuation of the weather used for the past period. From fertiliser application to the
end of the forecast period, realized weather comprised weather that varied from the forecast weather by an
amount depending on the forecast accuracy, but beyond the end of the forecast period weather having a random
relation to the forecast weather was used. Thus during the forecast period, the forecast weather represents a
known accuracy forecast of the realized weather, and after the forecast period, represents a zero-accuracy
forecast.
The simulations
For a particular cropping and soil type, a simulation was performed on the forecast weather which provided the
optimum fertiliser recommendation for those conditions (this is the combination of fertiliser amounts and
timings that gives the largest achievable crop uptake using the smallest total fertiliser amount). Using the same
fertiliser amounts and timing, a simulation was performed on the realized weather. The simulations thus yield
pairs of results, one for forecast and one for realized weather. Similar pairs of simulations were performed for
each forecast accuracy and duration. For each site, soil type and cropping, the above procedure was replicated
80 times, for different sets of weather data.
Estimation of economic benefits
The cost to the farmer of lost yield was calculated using the following sources of information. The ratio of yield
N to crop N obtained from Sundial crop parameters. Product prices for April 2002 from: Farmers Weekly (5
April 2002). Nitrogen and Dry Matter content for oilseed rape from: A. Macdonald, IACR Rothamsted, 2002,
personal communication. Crude Protein and Dry Matter contents for other crops from: Agro Business
Consultants, 1998. Crop areas for England from: DEFRA, 2002. Crop areas for Wales, year 2001 from: John
Bleasdale, Agricultural Statistics, Welsh Assembly Government (personal communication).
The values used are shown in the appendix.
8
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
RESULTS
The absolute quantities of nitrogen applied as fertiliser, lost by denitrification and by leaching, and taken up by
the crop, averaged over all soils and regions, for the forecast simulations only, are shown in Table 3. These are
given to provide a context to the changes in these amounts with forecast accuracy.
Table 3: Quantities of nitrogen (kg N ha-1) applied as fertiliser, lost by denitrification and by leaching, and
taken up by the crop. Mean for all soils and climates.
Crop
Winter Wheat
Spring barley
Winter OSR
Potatoes
Fertiliser applied
178
73
207
92
Denitrification
8
7
6
8
Leaching
20
21
10
23
Crop uptake
200
133
197
178
Results are expressed in general as the benefit from having prior knowledge of a given accuracy of the weather
forecast. The benefit has been quantified as kg N ha-1 (crop offtake, avoided leaching or denitrification), or as
cash. The number of paired simulations performed, given by:crops x locations x soils x (accuracies x durations
+ 1) x replications, is over 1/3 million, so we have summarised the most important results for this report.
Losses of N
Increased prior knowledge had a small direct environmental benefit by decreasing the amount of N leached and
of that denitrified. denitrified before harvest. This is not unexpected as only a small proportion of the leaching
and denitrification losses occur during the growing season. If calculations were extended over the following
winter, more significant results may have been observed.
The reduction in losses by denitrification was in no case statistically significant (Table 4). With perfect
knowledge of the weather for three or more weeks into the future, denitrification was reduced by an average of
only 0.1 kg N ha-1.
The mean decrease in leaching of nitrogen due to the maximum level of prior knowledge, for all soils, sites and
crops, was 1.1 kg N ha-1. The wetter areas show the largest decreases in leaching with increased prior
knowledge (Table 5). Of the crops, winter wheat show the largest, and potatoes the smallest, reduction in
leaching (Table 6). There is much variation between years but even in the most extreme circumstances the
maximum level of prior knowledge did not decrease leaching by more than 5.4 kg N ha -1 (mean decrease for
winter wheat on loam at Bala). The decreases in leaching are in the majority of cases not statistically
significant and unlikely to be detectable against background losses.
9
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
Table 4: Mean increase (kg N ha-1) in denitrification due to no knowledge, compared with perfect knowledge of
future weather.
Temperature Rainfall
Means
Wet
Moderate Dry
Warm
0.0
0.0
0.0
-0.2
0.2
Moderate
-0.1
-0.1
-0.2
Cool
-0.3
0.1
0.0
-0.0
0.2
Means
-0.1
-0.1
0.1
No values significant at the 95% confidence level
(95% confidence interval =+- 0.34)
Table 5: Mean increase (kg N ha-1) in leaching due to no knowledge, compared with perfect knowledge of
future weather.
Temperature Rainfall
Means
Wet
Moderate Dry
Warm
2.6*
0.4
1.5
1.6*
1.1*
Moderate
0.9
0.7
0.4
Cool
2.2
1.3*
1.4
0.8
1.1*
Means
1.8
0.7
1.0
* Significant at the 95% confidence level
Table 6: Mean increase (kg N ha-1) in leaching due to no knowledge, compared with perfect knowledge of
future weather.
Crop
Soil type
Means
Sand
Loam
Clay
Winter Wheat 1.3
1.5*
1.5*
1.5
Spring barley 0.9
1.1
1.0
1.0
Winter OSR
1.1
1.2
1.1
1.1
Potatoes
0.7
0.9
0.6
0.7
Means
1.0
1.2
1.0
* Significant at the 95% confidence level
10
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
Crop N offtake
Crop N offtake was increased relative to the yield from the optimum rate recommended by SUNDIAL by prior
knowledge (Figure 1). With knowledge of the weather to come for up to 3 weeks in advance that is only 60%
accurate, an increase of 1 kg N ha-1 can be expected on average in arable yields in England and Wales. A
forecast that is 100% accurate could push that gain up to 2 kg N ha-1. A 7 week forecast that is 70 or 80%
reliable could also increase N offtake (that is, avoided shortfall in N offtake) by 2 kg N ha-1. These amounts are
not large but are average effects over the 80 simulation years and represent a consistent benefit. There is
variability between the simulation years so that in some the benefit was more (see next section). The benefit
was little different between soil types (range 5.8 to 6.6 kg ha-1) but more by crop (Spring Barley 4.7, winter
wheat 7.2 kg N ha-1). Perhaps surprisingly there was little appreciable difference ascribable to the region and
its weather (Table 7). This may be because the analysis compares differences in prior knowledge within
regions of given weather. It should be noted, however that these data include weather data from the whole of
England and Wales but arable cropping is sparse in the wet, west represented by Carlisle, Bala and Starcross.
Figure 1: change in crop N (kg N ha-1) offtake due to increase in prior knowledge of weather relative to no
forecast. Durations (Dur) refer to length of forecast, accuracy is the reliability of that forecast.
8
Crop N offtake kg/ha
7
6
5
Dur
Dur
Dur
Dur
Dur
Dur
Dur
4
3
2
1
0
0
20
40
60
80
100
Accuracy of Forecast %
11
0
1
3
5
7
9
27
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
Table 7: Mean benefit (kg N ha-1) in crop offtake using a 27 week forecast at 90% accuracy
Temperature Rainfall
Wet
Moderate Dry
Warm
8.1
5.7
6.0
6.0
Moderate
5.3
6.6
Cool
6.3
6.6
6.9
4.9
Means
6.8
5.9
5.8
Means
6.4
5.9
6.2
Figure 2: Frequency with which weather forecasts for the specified duration and accuracy would increase crop
N offtake by 12.5 kg N ha-1 (0.5 tonne cereal yield). Averaged over all crops and weather stations
25
Dur0
Dur 1
Dur3
Dur5
Dur7
Dur9
Dur27
Frequency (%)
20
15
10
5
0
0
20
40
60
80
100
Forecast accuracy (%)
Variability between years
The results presented so far represent averages over the whole of the 80 generated years of weather for the 10
weather sites studied. Variability ensures that in some years the losses were greater or less. However, the
distribution of crop N offtake over these 80 years was somewhat skewed because shortfalls were sometimes
much less than the average, whereas gains greater than average were rather small (Figure 3). To make the
consequences of this clear we have plotted the frequency that losses of crop N offtake exceeded 12.5 or 25 kg N
ha-1 (Figures 2 & 4). Note that percentage values are plotted, thus the data shown represent the number of
occasions that such a loss or greater would occur in 100 years. The cut-off values of 12.5 and 25 kg N ha-1 are
roughly equivalent to a loss of half or one tonne ha-1 of cereal yield respectively. For consistency’s sake these
same amounts of N were applied also to the losses of oil seed rape and potatoes even though such amounts of N
correspond to different amounts of yield in these crops.
12
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
Shortfall (gain) in crop uptake kg/ha
Figure 3: Shortfall or gain in N uptake by a crop of winter wheat in 80 separate realisations of weather,
forecast with the specified accuracy and duration.
10
5
0
-5 1
11
21
31
41
51
61
71
Dur 27, Pfa 90%
Dur 1, Pfa 100%.
Dur 5, Pfa 50%.
Zero accuracy
-10
-15
-20
-25
-30
-35
Ranked order
Although the extra amounts of N acquired by a crop are relatively small in comparison to the amount of
fertiliser applied (Figures 2 & 4), 1 tonne of yield is a significant proportion of a farmer’s yield and would
represent almost pure profit at this level. Taken over the whole country these benefits could be considerable.
A forecast of 27 weeks (i.e. for the whole of the growing season following application) of 90% accuracy would
be worth more than £55M to farmers each year on average (Table 8). In one of the more extreme years referred
to in Figures 2 & 4 the amount of money could be even more. Even modest, and more realistic levels of prior
knowledge (say 3 or 5 weeks forecast of 50% reliability) could be worth £8-10M (Table 8). An increase in
reliability to 70% might be worth £15-18M to the industry on average each year. Foresight (Anon, 2001) has
suggested that forecasts of 2-3 months duration might be useful in many contexts. To arable crop farmers in
England and Wales they could be worth up to £15M each year on average. This amount could be even more if
an accuracy greater than 70% proves to be achievable. These calculations have excluded the wettest regions of
England and Wales because relatively little acreage of arable crops is grown in these regions and the greater
rainfall tends to make the losses (N-offtake, denitrification and leaching) greater. We felt that to include
simulations based on these results would bias the financial data. The complete acreage of the four arable crops
in England and Wales was used in the calculations, however. Note that all other averaged data in this report
does include the western weather stations.
These analyses are expressed in terms of the financial benefit to be gained from medium-term weather forecasts
where farmers follow the advice given by SUNDIAL. The results bear a different interpretation, however,
which is that farmers are behaving rationally in consistently over-fertilising crops e.g. Whitmore and van
Noordwijk (1995). There are benefits to be gained in some years by applying too much nitrogen fertiliser and
occasionally these benefits can be quite large. The financial gains referred to in this report should be seen as
potential benefits within a framework of strict adherence to environmental constraints. Given the large increase
in the area of the UK recently designated as nitrate vulnerable, this does not seem unreasonable.
13
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
Table 8: Gross value (£M, through avoided loss of yield) of prior of weather for the specified duration and
accuracy. The three most westerly weather stations were excluded and the calculations performed for arable
land growing four major crops. The calculations take no account of the extra cost of chemicals needed to avoid
this loss of yield.
Accuracy
Weeks prior 100% 90%
knowledge
1
15.3 13.0
3
23.0 20.0
5
28.2 24.9
7
32.6 28.9
9
37.2 33.0
27
70.4 56.2
80%
70%
50%
30%
11.0
17.4
21.5
25.2
28.0
44.1
9.4
15.1
18.5
21.1
23.3
36.4
4.7
8.4
10.6
12.6
14.7
20.4
2.9
6.3
7.5
7.9
8.2
15.2
Figure 4: Frequency with which forecasts of weather for the specified duration and accuracy would increase
crop N offtake by 25 kg N ha-1 (1 tonne cereal yield). Averaged over all crops and weather stations
4.5
Frequency (%)
4
Dur0
3.5
Dur 1
3
Dur3
2.5
Dur5
2
Dur7
1.5
Dur9
1
Dur27
0.5
0
0
20
40
60
80
Forecast accuracy (%)
14
100
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
CONCLUSIONS AND RECOMMENDATIONS
Losses
The reduction in losses of N via denitriification or leaching as a result of reliable medium term weather
forecasts is unlikely to be great. This is not a surprising result because these losses occur in wet soil, which is
increasingly unlikely to be the case as the growing season progresses
Crop N offtake
The benefits of medium-term weather forecasts on crop N offtake do not at first sight appear to be considerable.
If a reliable forecast was available for 27 weeks these benefits would be about 6 kg N ha-1 on average. These
amounts represent about one quarter of one tonne of cereal per hectare. Importantly, however, they are
systematic differences, i.e. consistent increases. There were few years in which there was a penalty for using
the improved forecast (Figure 3) and there were many years in which the benefit was greater (20 years per
hundred better than 12.5 kg N ha-1 or half a tonne extra cereal at all sites, 4 years per hundred 25 kg N ha-1 or
one tonne extra cereal yield per hectare, Figures 2 & 4).
Economic benefits
The gains in crop N offtake look substantial when expressed nationally in financial terms. The benefits of even
a limited weather forecast (three-week forecast that is 50% accurate) could be worth £8M per year to an
industry adhering strictly to a nitrogen prediction system. A rather better forecast (27 weeks, 90% reliability)
might be worth more than £55M per annum. These benefits quantify the extra value of yield in relation to
nitrogen fertiliser only and make no attempt to assess the value of better pest and disease predictions or of the
timing and scheduling of operations.
Further work
This scheme has not investigated grassland. Applications of grassland are much greater and they are made in
the warmer and moister parts of the country where losses are likely to be greater than the average of this
investigation and where benefits might be more noticeable. We would be prepared to carry out such an
investigation in conjunction with IGER if DEFRA thought it appropriate.
The losses presented here are for spring-summer estimated by optimisation of the SUNDIAL model. Losses
will be far more appreciable during autumn and winter. Prior knowledge with regard to loss could be beneficial
in these months in terms of scheduling operations such as establishing or ploughing a cover crop or even the
choice of such a cover crop, or when to cultivate in spring. This information could be especially valuable in
nitrate-vulnerable zones. If DEFRA feel it appropriate we could investigate the potential benefits of prior
knowledge before the winter months on nitrate leaching and denitrification.
The simulations and the calculations made from them cover England and Wales only. The financial benefit
would probably be greater if the arable parts of Eastern Scotland were also included. We have studied winter
wheat, spring barley, oil seed rape and potatoes only. The financial benefits to the industry would be greater if
the system were to be used with other arable crops and for horticulture also.
15
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
Development of a capability to predict the weather in the medium term will cost resources and this cost must be
set against the gain. There are many other potential beneficiaries of the kind of weather prediction set out in
Foresight. The gain to arable farming in England and Wales is likely to be about £15M per year less the
additional cost of the N fertiliser and any additional sprays needed.
REFERENCES
Agro Business Consultants (1998) Agricultural Bugeting & Costing Book No 47
Anon (2001) Seasonal weather forecasting and the food chain www.foresight.gov.uk
Bradbury, N.J., Whitmore, A.P., Hart, P.B.S. and Jenkinson, D.S. (1993) Modelling the fate of nitrogen in crop and
soil in the years following application of 15N-labelled fertilizer to winter wheat. Journal of Agricultural
Science, Cambridge 121, 363-379.
Buzzi, A., Fantini, M., Malguzzi, P., Nerozzi, F., 1994. Validation of a Limited Area Model in Cases of
Mediterranean Cyclogenesis: Surface Fields and Precipitation Scores. Meteorology and Atmospheric
Physics, 53, 137-154
DEFRA, 2002. Agricultural and horticultural census: 5 June 2002 England. National Statistics.
Farmers Weekly 12 July 2002 137, no.2Gabriel, K.R. and Neumann, J., 1962. A Markov chain model for daily
rainfall occurrence at Tel Aviv. Quarterly Journal of the Royal Meteorological Society 88, 90-95
MAFF (2000) Fertiliser Recommendations for Agricultural and Horticultural Crops. RB209, 7th Edition. The
Stationery Office, London.Monteith, J. L. (1965) Evaporation and Environment. 19th Symposia of the
Society for Experimental Biology, Cambridge University Press. 19, 205-234
NT2306, Final report to DEFRA: Follow-up to System for Improved Fertilizer Recommendation for Arable
Farmers and Horticultural Growers. 2002.
NT2307, Final report to DEFRA (2000) The influence of weather patterns on fertiliser recommendations and
risk assessment.
Rogers, E., T. L. Black, D. G. Deaven, G. J. DiMego, Q. Zhao, M. Baldwin, N. W. Junker, and Y. Lin, 1996:
Changes to the operational "early" Eta analysis/forecast system at the National Centers for Environmental
Prediction. Wea. Forecasting, 11, 391-413.
Smith, J.U., Bradbury, N.J. & Addiscott, T.M. (1996) SUNDIAL: A PC-based system for simulating nitrogen
dynamics in arable land. Agronomy Journal 88:38-43.
Whitmore, A.P. and Van Noordwijk, M. (1995) Bridging the gap between environmentally acceptable and
agronomically desirable nutrient supply. In: Ecology and Integrated Farming systems, D.M. Glen, M.P.
Greaves and H.M. Anderson (eds.), pp 271-288, John Wiley and Sons, Chichester.
16
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
APPENDIX:
Calibration of Pfa against the measured accuracy of forecasts.
We used the following methods to quantify the accuracy of forecasts and to calibrate the forecast accuracy
parameter, Pfa.
The Equitable Threat Score, ETS (Rogers et al., 1996) was used to measure the accuracy of forecast of rain.
This method is suitable for paired observations and forecasts of a discrete variable, in this case precipitation,
above a specified threshold, and is defined as
ETS = (H-Ch ) / (F +O -H -Ch)
where H is the number of forecasts for which precipitation over a specified threshold was correctly forecasted
(number of “hits”), F is the number of forecasts for which precipitation was above the threshold, O is the
number of observations for which precipitation was above the threshold, and Ch is an estimate of the number of
forecasts that would be correct if made by chance, given by:
Ch = (F×O) / Nf
where Nf is the total number of forecasts.
A sample of perfect forecasts would achieve an equitable threat score of 1.0 and forecasts based on chance
would have an ETS of 0.0. Forecasts poorer than that expected for chance have a negative ETS.
In this work, the median rainfall at each site was chosen as the threshold for computing ETS.
The accuracy of forecasts of ET and Tmean were measured using root mean square error (RMSE, Buzzi et al.,
1994) of observed in relation to forecast weather. RMSE is suitable for paired observations and forecasts of a
continuous variable, which has an approximately normal distribution. The method is therefore not suitable for
rain, which has an extremely skewed distribution.
RMSE was calculated for each week, and presented as the mean for all weeks and all sites. The same data are
also presented as RMSE divided by RMSE of the random (Pfa=0) forecasts to give a normalised value,
RMSEnorm, with a range of zero for sample of perfect forecasts, and of 1.0 for forecasts based on chance.
17
Project
title
How far will medium term weather forecasts improve
assessment of risks?
MAFF
project code
ES0101
Appendix Table 1
ETS
SD between sites
RMSE
RMSE
RMSEnorm
RMSEnorm
Pfa
Rain
1
1
0
Mean for ET
0
Mean for TAve 0
Mean for ET
0
Mean for TAve 0
0.9
0.885
0.004
0.652
0.443
0.156
0.142
0.8
0.778
0.004
1.125
0.816
0.273
0.262
0.7
0.68
0.003
1.574
1.169
0.384
0.374
0.5
0.5
0.004
2.387
1.813
0.585
0.581
0.3
0.346
0.006
3.06
2.351
0.752
0.753
0
0.005
0.007
4.07
3.126
1
1
The relationships between Pfa and the measures of forecast accuracy are not totally linear (Appendix Figures
A1 and A2). However the values of Pfa used were sufficiently close to the measured forecast accuracies to be
used directly in this study.
The quality of generated data.
The quality of data produced was assessed for Rothamsted weather with different values of Pfa.
Bias was less than ±0.01 for all variables. Persistence of wetter or drier weather between weeks was not well
reproduced at the lower accuracy forecasts. With for a Pfa of 0.3, persistence was 0.52, instead of 0.57. The
distribution of rain amounts was reproduced accurately.
Appendix Table 1: Values used for calculating economic cost to the farmer.
Feed Wheat Feed barley Oilseed Rape Potatoes
Yield N :Crop N ratio
0.727
0.697
0.783
0.735
DM content, g/kg
857
864
1000
204
Crude protein, g/kg DM
128
129
220
108
Farm-gate price, £
67
57
135
93
Appendix Table 2: Areas of crops grown in the England and Wales
Area grown
Wheat Other cereals Oilseed rape Potatoes Arable crops not
1000 ha
in study
England, 2002
1891
838
320
122
575
Wales
11
31
1
3
18
18
How far will medium term weather forecasts improve
assessment of risks?
Project
title
MAFF
project code
ES0101
Appendix Figure A1
Equitable Threat Score of rain, ETS v forecast accuracy parameter, Pfa. Mean of weekly values for all sites.
The median rainfall for each week and site was used as the threshold.
1
RMSEnorm
0.8
0.6
ET
Tmean
Perfect fit
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
Forecast accuracy parameter (Pfa)
Appendix Figure A2
Normalised root mean square error, RMSEnorm v forecast accuracy parameter, Pfa. Values used are means of
RMSEnorm for all weeks and sites. Error bars are the standard deviation between sites of the mean RMSEnorm
for all weeks.
Equitable threat score, ETS
1
0.8
0.6
Rain
Perfect fit
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
Forecast accuracy parameter, Pfa
Please press enter
19