Food Security

, Volume 2, Issue 2, pp 143–156 | Cite as

The impact of climate change on disease constraints on production of oilseed rape

  • Neal Evans
  • Michael H. Butterworth
  • Andreas Baierl
  • Mikhail A. Semenov
  • Jon S. West
  • Andrew Barnes
  • Dominic Moran
  • Bruce D. L. Fitt
Original Paper

Abstract

Weather data generated for different parts of the UK under five climate change scenarios (baseline, 2020s low CO2 emissions, 2020s high emissions, 2050s low emissions, 2050s high emissions) were inputted into weather-based models for predicting oilseed rape yields and yield losses from the two most important diseases, phoma stem canker and light leaf spot. An economic analysis of the predictions made by the models was done to provide a basis to guide government and industry planning for adaptation to effects of climate change on crops to ensure future food security. Modelling predicted that yields of fungicide-treated oilseed rape would increase by the 2020s and continue to increase by the 2050s, particularly in Scotland and northern England. If stem canker and light leaf spot were effectively controlled, the value of the crop was predicted to increase above the baseline 1980s value by £13 M in England and £28 M in Scotland by the 2050s under a high CO2 emissions scenario. However, in contrast to predictions that phoma stem canker will increase in severity and range with climate change, modelling indicated that losses due to light leaf spot will decrease in both Scotland and England. Combined losses from both phoma stem canker and light leaf spot are predicted to increase, with yield losses of up to 40% in southern England and some regions of Scotland by the 2050s under the high emission scenarios. For this scenario, UK disease losses are predicted to increase by £50 M (by comparison with the baseline losses). However, the predicted increases in fungicide-treated (potential) yield and phoma stem canker/light leaf spot yield losses compensate for each other so that the net UK losses from climate change for untreated oilseed rape are small.

Keywords

Economic analysis Food security Global warming Light leaf spot Phoma stem canker Sustainability 

Introduction

In a world where more than 1 billion people do not have sufficient food (Anon. 2009), the effects of crop diseases mean that there is less food to eat; crop losses from diseases are estimated at 16% globally, despite efforts to control them (Oerke 2006). The food security (Pinstrup-Andersen 2009) problems caused by crop diseases are especially severe in the developing world, where crop losses can lead to starvation for subsistence farmers (Schmidhuber and Tubiello 2007). In the past, farmers grew ‘land races’ of crops that were less susceptible to major losses from diseases because they were genetically variable, having often co-evolved with the pathogens at their centres of origin (Stukenbrock and McDonald 2008). Now, the demand for higher yields has led to replacement of land races by genetically uniform crops that are more susceptible to diseases because the variable pathogens can rapidly adapt to render ineffective the host genes for resistance. For example there were ‘boom and bust’ cycles in the first half of the 20th century in North America as new races of Puccinia graminis (the cause of stem rust) evolved to counter new resistance genes in commercial wheat cultivars (Carleton 1915). The food security problems associated with crop diseases are now becoming more acute due to climate change (Anderson et al. 2004; Chakraborty et al. 2000; Garrett et al. 2006; Gregory et al. 2009; Stern 2007), especially for farmers in marginal areas such as sub-Saharan Africa (Schmidhuber and Tubiello 2007). To guide government food security policy and planning for adaptation to climate change, there is a need to evaluate impacts of climate change on disease-induced losses in crop yields.

Oilseed rape (Brassica napus, B. rapa, B. juncea, B. carinata, rapeseed, canola) is grown throughout the world as a source of oil and protein (for human/animal consumption) and fuel (e.g. as a component of biodiesel). Worldwide, oilseed rape was the third most important source of vegetable oil and the second most important source of protein meal in 2000 (www.pecad.fas.usda.gov/cropexplorer/). Global production of oilseed rape has been increasing through increasing area and yield, with a total yield of 46 M t produced during the 2005/2006 growing season (http://faostat.fao.org/), worth £9200 M at a price of £200 t−1. Oilseed rape provides an essential source of oil and protein for subsistence farmers in China, India and parts of Africa (e.g. Ethiopia). One disease of worldwide importance on oilseed rape is phoma stem canker (blackleg, caused by Leptosphaeria maculans), which results in losses amounting to more than £500 M per season through severe epidemics in Europe, North America and Australia, and is spreading globally, threatening production in India, China and Africa (Fitt et al. 2006; Fitt et al. 2008). Another disease of importance in northern Europe is light leaf spot (caused by Pyrenopeziza brassicae) (Boys et al. 2007; Gilles et al. 2000a). These are the two most important diseases of oilseed production in the UK, where yields are generally >3 t ha−1, with phoma stem canker currently being more important in southern England and light leaf spot being more important in northern England and Scotland (www.cropmonitor.co.uk; Fitt et al. 1998). It is predicted that climate change will increase the range and severity of phoma stem canker epidemics (Butterworth et al. 2010; Evans et al. 2008). This paper examines the economics of the impacts of climate change on crop losses to diseases, using phoma stem canker and light leaf spot of winter oilseed rape in the UK as an example.

Materials and methods

Phoma stem canker and light leaf spot on winter oilseed rape in the UK

In the UK, winter oilseed rape crops are autumn-sown in August/September, and remain in a vegetative phase of growth in winter, flowering in April/May (spring) with harvest in July (summer) (Fig. 1). Crops are mostly grown in the eastern halves of England and Scotland because the terrain and soil fertility are less suitable for arable crop production further west. Phoma stem canker (L. maculans) epidemics are started by air-borne ascospores which germinate and produce leaf spots in autumn (October/November) (Biddulph et al. 1999; West et al. 2001). The disease is monocyclic (i.e. one cycle per growing season; little evidence for secondary disease spread). The pathogen then grows symptomlessly along the leaf petiole to reach the stem. Stem cankers are observed in April/May and become more severe by harvest, inducing yield loss by causing premature senescence and lodging. The disease is most severe in southern England. Light leaf spot (P. brassicae) epidemics are also started by air-borne ascospores in autumn (Fitt et al. 1998). However, the disease is polycyclic with an estimated 17 cycles during a growing season induced by secondary splash-dispersal of conidia produced on infected leaves (Gilles et al. 2000b, 2001b) to produce patches of diseased plants (Evans et al. 2003). These conidia spread the disease during the winter and spring, with symptoms observed on leaves, stems and pods. The disease causes yield loss by decreasing plant growth in winter and by damaging pods in summer (Boys et al. 2007; Gilles et al. 2000a). In comparison to L. maculans, P. brassicae develops under cooler, wetter condition and is therefore more severe in Scotland and northern England (Fitt et al. 1998). Since there is limited spatial spread for both diseases, other than at the start of the season when airborne ascospores are released, models have been were developed to provide information on the timing of spore release from diseased crop debris, based on temperature and wetness duration at different locations (Gilles et al. 2001a; Huang et al. 2007).
Fig. 1

Production of winter oilseed rape in relation to development of phoma stem canker and light leaf spot diseases in the UK. Crops are autumn-sown in August/September, and remain in a vegetative phase of growth in winter, flowering in April/May (spring) with harvest in July (summer). Phoma stem canker (Leptosphaeria maculans) epidemics are started by air-borne ascospores (a) which germinate and produce leaf spots (b) in autumn (October/November). The disease is monocyclic (one cycle per growing season). The pathogen then grows symptomlessly along the leaf petiole to reach the stem. Stem cankers (c) are observed in April/May and become more severe by harvest. They induce yield loss by causing premature senescence and lodging (d). The disease is most severe in southern England. Light leaf spot (Pyrenopeziza brassicae) epidemics are also started by air-borne ascospores (e) in autumn. However, the disease is polycyclic with several cycles during a growing season because secondary splash-dispersal conidia are produced on infected leaves (f). These conidia spread the disease during the winter and spring, with symptoms observed on leaves, stems and pods. The disease causes yield loss by decreasing plant growth in winter (g) and by damaging pods in summer (h). It is most severe in Scotland and northern England

Climate change scenarios and oilseed rape yield predictions

Daily site-specific climate scenarios generated were based on the UKCIP02 climate change projections (Semenov 2007) from the HadCM3 global climate model (Collins et al. 2001) and global IPCC emission scenarios (Nakicenovic 2000). There were five simulated climate scenarios; baseline (1960–1990) and 2020HI, 2050HI, 2020LO and 2050LO for high and low CO2 emissions for the 2020s and 2050s. Simulated daily weather data for 30 years were generated for the five climate scenarios by a stochastic weather generator (LARS-WG, Semenov and Barrow 1997) for 14 sites across the UK. Data generated were daily minimum temperature, maximum temperature, rainfall and solar radiation. These simulated weather data for different climate scenarios were used as the inputs into weather-based models for predicting fungicide-treated winter oilseed rape growth and yield (STICS, Brisson et al. 2003), the severity of phoma stem canker disease (PASSWORD, Evans et al. 2008) and the incidence of light leaf spot disease (PASSWORD, Welham et al. 2004).

STICS model version 6.2 (Brisson et al. 2003; Brisson et al. 2002) (http://www.avignon.inra.fr/stics) was used to simulate yield of winter (autumn-sown) oilseed rape for each of the 14 sites and five climate scenarios for fungicide-treated crops. The inputs into the model were the CO2 concentrations and the UKCIP02 daily site-specific weather data generated for the five climate scenarios with the corresponding CO2 concentrations (Butterworth et al. 2010). These inputs were used to estimate site-specific yields. Since the STICS model was developed for French oilseed rape crops, the sowing date and radiation use efficiency (RUE) were adjusted to calibrate it for UK crops. The typical sowing date was set to 23 August and the typical harvest date was set to 15 July. The RUE parameters were increased by 36% to produce an average yield for the UK baseline scenario (1960–1990) of approximately 3 t ha−1 (www.hgca.com). The STICS model was calibrated for crops sprayed with fungicides to control diseases; since fungicides do not completely prevent yield loss from disease, the model underestimates the potential yield of the crop.

Phoma stem canker/light leaf spot yield loss predictions

The phoma stem canker model of Evans et al. (2008) was used to estimate the severity at harvest of phoma stem canker for each of the 14 sites and the five climate change scenarios. The date of crop establishment was estimated as 26 September, which is compatible with 23 August sowing date used by the STICS model. The stem canker model operates by first predicting the start date of leaf spotting in autumn using the mean maximum daily temperature and total rainfall after previous harvest when the pathogen begins to develop on crop debris (West et al. 2001). The onset of phoma canker on stems in the following spring is predicted from the start date of leaf spotting and the accumulated thermal time in °C-days. The increase in the severity of these cankers in the period until harvest is predicted from the date of onset of cankers using the subsequent accumulated thermal time which affects the colonisation of stem tissues by the pathogen. Disease severity values were generated for the predicted canker severity at harvest. The canker severity scores predicted were on a 0–4 scale (Zhou et al. 1999). The model was used to estimate phoma stem canker severity scores for each site and climate scenario. These data were averaged by calculating the median values. The model was run for cultivars with an average HGCA rating for resistance to L. maculans (www.hgca.com )(Evans et al. 2008).

Using the stem canker severity scores produced by this model, yield losses from phoma stem canker were estimated using a yield loss model (Butterworth et al. 2010). This relates canker severity at harvest to the associated yield loss for UK winter oilseed rape by a linear equation:
$$ {Y_c} = 99.1 - 15.2S $$
(1)
where Yc is the yield of crops with stem canker expressed as a percentage of the maximum potential yield produced in fungicide-treated crops and S is canker severity score (0–4 scale). The data used to construct (Fig. 2) and validate (Fig. 3) this model were from winter oilseed rape experiments in England. Those used to estimate this relationship were from experiments with 20 winter oilseed rape cultivars and unsprayed/fungicide sprayed plots harvested at Rothamsted in 2006 and 2007 (Table 1). The data used to validate the relationship between canker severity and associated yield loss were from a UK winter oilseed rape experiment harvested at Withington (1993) with one cultivar and 22 fungicide treatments (Butterworth et al. 2010) and from 23 experiments with unsprayed/fungicide-sprayed plots harvested at Boxworth (1997–2002), Rothamsted (1997 and 2000) or High Mowthorpe (2001) (Table 1). The yield loss was estimated by comparing yields of plots treated with fungicide with those of untreated plots from winter oilseed rape experiments where phoma stem canker was the main disease present with other diseases present at trace levels. Since the fungicide treatment did not provide complete control, this model underestimates the yield loss caused by the disease.
Fig. 2

Relationship between yield (Yc, as a percentage of the maximum potential yield produced in fungicide-treated plots) and severity of phoma stem canker (S, 0–4 scale according to the percentage of the stem circumference girdled by lesions (0 = no symptoms visible, 1 = 0–25% affected, 2 = 25–50%, 3 = 50–75%, 4 = 75–100%), Zhou et al. 1999 ) recorded on fungicide-untreated winter oilseed rape at Rothamsted in southern England in 2006 (20 cultivars) and 2007 (19 cultivars ) (Table 1). The cultivars differed in their resistance to Leptosphaeria maculans. Regression analyses of position and parallelism had shown that the data for the two growing seasons were fitted best by a single line rather than two lines, and other tests had shown that they were fitted better by a linear relationship than a curve (polynomial equation). The equation of the fitted regression line is: Yc = 99.1 −15.2S;  = 0.49; P < 0.001

Fig. 3

Validation of equation describing yield loss from phoma stem canker; regressions of observed against predicted yield (as a percentage of maximum potential yield from fungicide-treated plots) from phoma stem canker disease control experiments in England, for data sets independent of those used in model construction (Fig. 2). Predicted yields were estimated by inserting observed phoma stem canker severity score in untreated plots into the yield loss equation. Data for a) Withington in 1993, equation of fitted line: Yo = 0.85 Yp + 9.13;  = 0.58; P < 0.001.; b from a range of experiments, equation of fitted line Yo = 0.97 Yp - 15.7;  = 0.41; P < 0.001. Details of experiments are given in Table 1

Table 1

Details of winter oilseed rape field experiments in England, including locations, cultivars and harvest seasons, that provided data on severity of basal phoma stem canker and yield of plots with or without fungicide treatments that were used to construct and validate the model for describing the relationship between canker severity and yield loss (yield of plots with severe canker was expressed as a percentage of the yield of plots in which the disease was controlled by the fungicide programme1

Location

Harvest season(s)

Cultivars (resistance rating at time2)

No. fungicide treatments3

Purpose

Boxworth

1997

Rocket (4)

2

Model validation

Boxworth

1998

Apex (6) (3 experiments)

2

Model validation

Bristol (5)

Boxworth

1999

Apex (6) (2 experiments)

2

Model validation

Boxworth

2000

Pronto (5) (3 experiments)

2

Model validation

Boxworth

2001

Apex (6) (2 experiments)

2

Model validation

Pronto (5) (4 experiments)

Boxworth

2002

Apex (6) (2 experiments)

2

Model validation

Pronto (5)

Escort (7)

High Mowthorpe

2001

Pronto (5)

2

Model validation

Rothamsted

1997

Capitol (6)

2

Model validation

Rothamsted

2000

Apex (6)

2

Model validation

Rothamsted

2006

20 different cvs (3–8)4

2

Model construction

Rothamsted

2007

19 different cvs (3–8)4

2

Model construction

Withington

1993

Envol (5)5

22

Model validation

1Only experiments in which the optimum fungicide treatment programme provided good control of phoma stem canker were used for model construction or validation

2Numbers in parentheses indicate cultivar rating for resistance to L. maculans from the HGCA Recommended List for winter oilseed rape for that season, or its predecessors (9, very resistant; 1 very susceptible, www.hgca.com). Cultivars with a rating of 1–5 were regarded as susceptible and cultivars with a resistance rating of 6–9 as resistant

3For most experiments, there was a comparison between untreated plots and plots that received a full disease control programme consisting of autumn and spring treatments. However for the Withington 1993 experiment, there were 22 different fungicide treatments, including untreated. Canker symptoms were assessed in late June/ early July using the 0–4 scale (Zhou et al. 1999)

4Cultivars: Apex, Bristol , Canberra, Castille, Courage, Disco, Elan, ES Astrid, Escort, Expert, Fortis, Hearty, Lioness, NK Bravour, NK Victory, Ontario, Recital, Royal, Shannon and Winner. Data from one cv (Hearty) in 2007 only, was omitted from model construction due to yield effects caused by light leaf spot, which was moderately severe on that cultivar only. Three replicated plots per cultivar were treated with an autumn foliar fungicide regime (2 sprays) and a further three replicates were untreated. Mean yields of the fungicide untreated plots per cultivar were expressed as ‘% yield,’ i.e. a percentage of the respective fungicide-treated plot yield for that cultivar. Phoma stem canker symptoms were recorded in mid-late June 2006 and 2007, using the HGCA 0–6 scale based on the extent of girdling by canker at the stem base (0 = uninfected, 1 = <25% stem circumference girdled, 2 = 26–50% girdled, 3 = >51–75% girdled, 4 = >76–100% girdled, 5 = 100% girdled + stem weak, 6 = plant dead or lodged) (http://www.oregin.info/information/sops/OSR_field_experiment.htm) and converted to the 0–4 scale (Zhou et al. 1999)

5Canker symptoms were assessed on 12 July using the 0–4 scale; more details of the different fungicide treatments are given in Zhou et al. (1999)

Light leaf spot incidence predictions for growth stage (GS) 3,3 (green flower bud)(Sylvester-Bradley and Makepeace 1985) for cultivars with average resistance to P. brassicae were generated for each of the 14 sites and five climate change scenarios using a weather-based model derived from the models developed by Welham et al. (2004):
$$ Logit(p) = 5\cdot0 - 0\cdot49{T_s} + 0\cdot022{R_w} $$
(2)
where p was predicted mean incidence of light leaf spot (percentage of plants affected) in spring at GS 3,3 and logit(p) = log(p/1-p)(data were logit-transformed to normalise the variance), Ts was mean temperature in the previous summer (July/August) before the crop was sown and Rw mean monthly rainfall during winter (December to February) when the crop was at the rosette growth stage. The predictions of light leaf spot incidence for each site and scenario were then used to calculate predicted yield loss for each site and scenario using a modified version of the yield loss model derived by Su et al. (1998).
$$ {Y_l} = 99.4 - 0.32p $$
(3)
where Yl is yield of crops with light leaf spot expressed as a percentage of maximum potential yield and p is predicted light leaf spot incidence under the different climate change scenarios. The predicted percentage yield losses for phoma stem canker and light leaf spot were then summed for each of the 14 sites and five climate change scenarios. Since the two diseases have different geographical distributions (light leaf spot predominant in the north, stem canker predominant in the south with few situations where the two diseases interact), and generally cause damage at different stages of crop growth, we assumed that losses would be additive (Zhou et al. 2000).

Regional treated and untreated yield predictions

Outputs from the oilseed rape model provided data on predicted effects of climate change on oilseed rape yields for the 14 sites across the UK for the five different climate change scenarios. For each site, results were mapped onto oilseed rape growing areas of the UK. Data for county/ regional boundaries and areas of oilseed rape grown in each county were from the 2006 Defra Agricultural and Horticultural Survey, available online at www.defra.gov.uk (Fig. 4). The assumption was made that the areas grown in future in each county/region will be the same as in 2006. The results were calculated at the county scale (e.g. Norfolk) and then presented at the geographic regional scale (e.g. East of England), and for England, Scotland and the UK. For each site, the yields were mapped onto the growing regions using a nearest point scheme. Where counties were approximately equidistant from two or more of the 14 sites, average yield data for the relevant sites were used. A county was considered approximately equidistant when the mid-point between any two sites lay within its boundary. Similarly the phoma stem canker and light leaf spot yield loss predictions were mapped onto these counties and regions. Fungicide-treated yield and yield loss data (for cultivars with average resistance) were then combined to estimate untreated yields for each scenario for each region.
Fig. 4

Government regions and sites for weather predictions. Map of England and Scotland, showing locations of 14 sites for which the stochastic weather generator produced weather data for five climate scenarios for UK government regions. Information about these regions is given on this web-site (http://www.statistics.gov.uk/geography/downloads/uk_gor_cty.pdf)

Economic evaluation

The average price per tonne of oilseed rape (£195.60 t−1) was obtained by taking a 4 year average from 2004–2008 (HGCA Market Data). All monetary figures are given at 2008 prices without applying the discount rate unless they are specified as present value figures. The price of oilseed rape has increased considerably since 2006 (now £270 t−1, www.hgca.com), due to other economic factors affecting the value of oilseed rape which are beyond the scope of this crop and disease based model, such as labour costs and the level of demand. We therefore assume that the relative costs involved and the level of demand remain constant in order to study the effects of climate change alone. Given the recent increase in prices and the predicted growth in demand through population growth, the predictions of this model underestimate the economic effects of climate change. Thus it is important to remember that these economic predictions are not predictions of actual states.

Values were calculated for each scenario for the total fungicide treated yield, the losses in yield caused by phoma stem canker and light leaf spot for representative cultivars with average resistance, and the untreated yield which was estimated by subtracting yield losses from stem canker and light leaf spot from the treated yield. The values were calculated by multiplying the total yield per region by the average price of £195.60 per tonne of oilseed rape. Present value figures were then calculated using the recommended discount rate of 3.5% for the 2020s scenarios and 3.0% for the 2050s scenarios (Anon. 2003). Present value figures represent the current worth of a future sum of money given a specified rate of return or discount rate. The present value of a future sum of money is the amount that one would pay today that is equivalent to the value of that amount of money on a specified future date. The discount rate represents the extent to which people prefer to receive a sum of money now rather than in the future. Present value figures can therefore be used to compare the economic values of scenarios in the future with those of today. These present value figures represent the value today of the effects of climate change in one particular year and not the present value of the total effects of climate change between now and the selected date.

Results

Oilseed rape yield predictions

The predictions obtained by inputting simulated climate data into the STICS model suggest that climate change will generally increase the yield of winter oilseed rape crops treated with fungicide to control diseases such as phoma stem canker and light leaf spot (Butterworth et al. 2010). The model predicts that in the 2020s and 2050s the greatest yields will be in eastern Scotland and north-east England. The increases in yield, by comparison with the baseline scenario, are generally greater for the high CO2 emissions scenarios than for the low emissions scenarios. The increases are greater for the 2050s than for the 2020s. The baseline (1960–90) yield indicates that the annual value of the total oilseed rape output for the UK, at a price of £195.60 t−1, was over £525 M (Table 2), if phoma stem canker and light leaf spot were controlled with fungicides. It is predicted that this value will increase under all climate change scenarios, with the greatest increases under high CO2 emissions and in Scotland rather than England, so that under the 2050HI emissions scenario, the value of the crop will be £13 M more than the baseline scenario in England and £28 M more in Scotland.
Table 2

Effects of climate change on the output of winter oilseed rape (treated with fungicide), calculated by region. The area grown per region (2006) and the predicted regional output are given for the baseline (1960–1990), 2020LO (low CO2 emissions), 2020HI (high emissions), 2050LO and 2050HI climate scenarios and presented in thousands of pounds (£000 s). The yield figures were calculated after interpolating the results from the oilseed rape yield predictions according to UK government region and then multiplied by an average price of £195.60 t−1

Regiona

Value of oilseed rape crop (£000 s)

Baseline

2020LO

2020HI

2050LO

2050HI

North East

14,098

13,168

14,536

14,646

14,812

North West

2,097

2,024

1,861

2,115

2,169

Yorkshire & Humberside

37,220

35,342

38,251

38,126

38,358

East Midlands

69,007

69,480

69,277

69,744

70,874

West Midlands

20,194

20,121

16,839

20,900

21,726

Eastern

63,885

63,854

63,661

65,792

66,907

London and South East

46,508

46,867

46,939

48,216

49,700

South West

26,742

26,831

26,873

27,570

28,538

England total

279,749

277,688

278,237

287,110

293,085

Scotland

245,587

257,258

262,986

269,480

273,770

UK total

525,335

534,946

541,224

556,590

566,855

Phoma stem canker and light leaf spot yield loss predictions

In contrast with the predicted increase in range and severity of phoma stem canker epidemics (Evans et al. 2008), the incidence of light leaf spot was predicted to decrease (Fig. 5). For example, in northern England and Scotland, the incidence of light leaf spot is predicted to decrease by 20–30% plants affected under predicted climate change scenarios for the 2050s (Fig. 5d & e). In the south of England, where the incidence of severe light leaf spot epidemics is currently small, it is predicted to decrease further. For example, predictions for Rothamsted in Hertfordshire were that the current incidence of ∼10% plants affected will decrease to 2.5% by the 2050s under a high CO2 emissions scenario (Fig. 6). Thus, by contrast with predicted increases in losses from phoma stem canker (Butterworth et al. 2010), losses from light leaf spot are predicted to decrease. However, combined yield losses from both diseases are predicted to increase across the UK under climate change scenarios for the 2020s (Fig. 7b & c) and the 2050s (Fig. 7d & e). Losses are predicted to increase to 40% in the main oilseed rape growing regions of southern England and north-east Scotland by the 2050s under the high CO2 emission scenario (Fig. 7e)
Fig. 5

Predicted incidence (% plants affected) of light leaf spot (Pyrenopeziza brassicae) at green flower bud (GS 3,3) of UK winter oilseed rape crops (cultivars with average disease resistance) for a) baseline 1960–1990, b 2020s low emissions, c 2020s high emissions, d 2050s low emissions and e) 2050s high emissions climate scenarios. Predicted incidences are interpolated from predictions for 14 sites across the UK

Fig. 6

Predicted incidence (% plants affected) of light leaf spot (Pyrenopeziza brassicae) at green flower bud (GS 3,3) on winter oilseed rape (cultivars with average resistance) for Rothamsted, Hertfordshire (latitude 0°21′W, longitude 51°48′N) for baseline 1960–1990, 2020s low, 2020s high, 2050s low and 2050s high emissions climate scenarios. Error bars are standard deviations

Fig. 7

Predicted yield loss (% of maximum yield from fungicide-treated plots ) from stem canker and light leaf spot (cultivars with average resistance) for a) baseline (1960–1990), b 2020s low emissions, c 2020s high emissions, d 2050s low emissions and e) 2050s high emissions climate scenarios. The maps are interpolated from yield loss data generated for 14 UK sites using weather-based disease models and yield loss models

It is estimated that the average annual losses caused by phoma stem canker and light leaf spot were worth £146 M under the baseline scenario (Table 3). It is predicted that climate change will increase these losses, with further losses of £6–8 M in England and £6–9 M in Scotland by the 2020s. By the 2050s, losses in England are predicted to increase by £16 M in the low emissions scenario and by £28 M in the high emissions scenario. This is in contrast to Scotland, for which losses are predicted to increase by £22 M above the baseline scenario for the 2050HI scenario and by £35 M for the 2050LO emissions scenario. The UK total losses are predicted to increase by £50 M from the baseline scenario in the 2050s.
Table 3

Effects of climate change on the losses from phoma stem canker and light leaf spot (for cultivars with average resistance) in winter oilseed rape crops not treated with fungicide. The values are given for the baseline (1960–1990), 2020LO (low CO2 emissions), 2020HI (high emissions), 2050LO and 2050HI climate scenarios and presented in thousands of pounds (£000 s). The figures were calculated after interpolating the results from the stem canker and the light leaf spot yield loss predictions according to UK government region and then multiplied by an average price of £195.60 t−1

Value of losses caused by phoma stem canker and light leaf spot (£000 s)b

Regiona

Baseline

2020LO

2020HI

2050LO

2050HI

North East

3,431

3,526

3,934

4,208

4,630

North West

520

533

501

602

676

Yorkshire & Humberside

7,804

8,118

9,074

9,661

10,874

East Midlands

15,116

16,869

17,567

18,871

21,748

West Midlands

5,038

5,539

4,716

6,244

7,308

Eastern

14,481

16,179

16,582

18,454

21,359

London and South East

12,388

13,540

13,874

15,381

17,882

South West

7,910

8,198

8,337

8,996

10,191

England total

66,690

72,502

74,584

82,417

94,668

Scotland

79,224

85,385

88,040

114,117

101,044

UK total

145,913

157,887

162,625

196,534

195,712

aGovernment regions can be found at http://www.statistics.gov.uk/geography/downloads/uk_gor_cty.pdf

bThe stem canker and light leaf spot loss predictions depend on the crop yield predictions in Table 2

Untreated oilseed rape yield predictions

The total area of oilseed rape grown in the UK in 2006 was over 850,000 ha, with most of the oilseed rape grown in the east of the country (Table 4; see HGCA website http://www.hgca.com/hgca/cerealsMap2007/areaMaps/oilseed_inset_map.gif). The baseline untreated winter oilseed rape production (regional total treated yield minus stem canker and light leaf spot yield losses) was greatest in eastern England. The East Midlands produced the most oilseed rape in England (>340,000 t) in the baseline scenario, whilst Scotland produced >1,300,000 t (only 60,000 t less than the total for England). The predicted effects of climate change in the 2020LO scenario are to decrease the untreated yields of all winter oilseed rape crops in England, by between 1.1% (South West) and 9.6% (North East). Conversely, the effect of climate change in Scotland will be to increase the yield by 3.3%, meaning that the UK total output only decreases by 0.6%. Under the 2020HI scenario, it is predicted that the untreated yield will decrease by more than in the 2020LO scenario in some English regions (e.g. North West) but by less in other regions (e.g. North East), so that the overall decrease by comparison to the baseline is similar for both scenarios. By contrast, in Scotland there will be a further predicted increase in yield (5.2% above the baseline), to give a UK total output only 0.2% below the baseline level. In the 2050LO scenario, the total production for England will be similar to that in the previous scenarios, but the production for Scotland will be 6.6% lower than in the baseline scenario. The total UK production in the 2050LO scenario is predicted to be 5.1% less than in the baseline scenario. In the 2050HI scenario, the predicted production will be less than in all other scenarios in England with a decrease of 6.9% from the baseline level. Scotland’s production will increase by 3.8% from the baseline level, giving an overall UK decrease of 2.2% in production by comparison with the baseline.
Table 4

Effects of climate change on the yield of untreated oilseed rape after phoma stem canker and light leaf spot losses, calculated by region. The area grown per region (2006) and the predicted total regional yield are given for the baseline (1960–1990) scenario. The total regional yield as a percentage of the baseline scenario is given for the 2020LO (low CO2 emissions), 2020HI (high emissions), 2050LO and 2050HI climate scenarios. The figures were calculated after interpolating the results from the treated oilseed rape yield predictions, the stem canker yield loss predictions and the light leaf spot loss predictions according to UK government region

Regiona

Area of oilseed rape grown (ha)b

Baseline total yield (t)

Yield (% of baseline yield)

2020LO

2020HI

2050LO

2050HI

North East

22,787

72,649

90.4

99.4

97.9

95.5

North West

3,601

10,769

94.5

86.2

96.0

94.7

Yorkshire & Humberside

61,068

189,125

92.6

99.2

96.8

93.4

East Midlands

113,479

342,630

97.6

96.0

94.4

91.2

West Midlands

34,419

97,510

96.2

80.0

96.7

95.1

Eastern

103,488

309,496

96.5

95.3

95.8

92.2

London and South East

79,063

219,128

97.7

96.9

96.2

93.3

South West

44,858

127,461

98.9

98.4

98.6

97.4

England total

462,764

1,368,770

96.3

95.6

96.1

93.1

Scotland

398,720

1,307,802

103.3

105.2

93.4

103.8

UK total

861,484

2,676,572

99.4

99.8

94.9

97.8

aGovernment regions can be found at http://www.statistics.gov.uk/geography/downloads/uk_gor_cty.pdf

bArea of winter oilseed rape grown in each region in harvest year 2006 (www.defra.gov.uk)

Thus it is estimated that the average annual value of the untreated UK oilseed rape production was £379 M for the baseline scenario (Table 5). It is predicted that under climate change the value of production will decrease in England by the 2020s and 2050s and increase in Scotland except for the 2050s under a low CO2 emissions scenario, when the total value of the crop is predicted to decrease by £19 M.
Table 5

Effects of climate change on the output of untreated winter oilseed rape (for cultivars with average resistance) after phoma stem canker and light leaf spot losses, calculated by region. The area grown per region (2006) and the predicted regional output are given for the baseline (1960–1990), 2020LO (low emissions), 2020HI (high emissions), 2050LO and 2050HI climate scenarios and presented in thousands of pounds (£000 s). The figures were calculated after interpolating the results from the treated oilseed rape yield predictions, the stem canker yield loss predictions and the light leaf spot loss predictions according to UK government region and then multiplied by an average price of £195.60 t−1

Regiona

Value of oilseed rape crop (£000 s)

Baseline

2020LO

2020HI

2050LO

2050HI

North East

10,666

9,642

10,602

10,438

10,182

North West

1,576

1,490

1,359

1,513

1,493

Yorkshire & Humberside

29,415

27,224

29,177

28,466

27,484

East Midlands

53,891

52,612

51,711

50,873

49,126

West Midlands

15,155

14,582

12,123

14,656

14,418

Eastern

49,404

47,676

47,080

47,338

45,548

London and South East

34,120

33,327

33,065

32,835

31,818

South West

18,831

18,633

18,536

18,574

18,348

England total

213,059

205,186

203,653

204,693

198,417

Scotland

166,363

171,874

174,946

155,363

172,726

UK total

379,422

377,060

378,599

360,056

371,143

aGovernment regions can be found at http://www.statistics.gov.uk/geography/downloads/uk_gor_cty.pdf

bArea of winter oilseed rape grown in each region in harvest year 2006 (www.defra.gov.uk)

The present value figures show that the estimated effects of climate change in the 2020s are worth between £5.2 M and £6.2 M of losses in England, between £3.6 M and £5.7 M of gains in Scotland and between £0.5 M and £1.6 M to the UK as a whole. The present value of losses in the 2050s is predicted to be less for England, between £2.4 M and £4.2 M, and the loss for Scotland from the 2050LO scenario is predicted to be £3.2 M and the gain from the 2050HI scenario predicted to be £1.8 M. The present value of the predicted effects of climate change to the UK as a whole ranges between £0.5 M in the 2020HI scenario and £5.6 M in the 2050LO scenario. These figures indicate that this model predicts that to mitigate the effects of climate change on oilseed rape in the UK in the 2050s, then it is worth spending between £2.4 M and £5.6 M today (Table 6).
Table 6

Present value of the effects of climate change on oilseed rape (for cultivars with average resistance) with phoma stem canker and light leaf spot. The values are given for the baseline (1960–1990), 2020LO (low emissions), 2020HI (high emissions), 2050LO and 2050HI climate scenarios and presented in thousands of pounds (£000 s). The figures were calculated after interpolating the predictions for oilseed rape yield and stem canker yield loss according to UK government region and then multiplied by a discounted average price. These data represent the present value of the predicted gains (+) or losses (−) (calculated as the difference from the baseline scenario) attributable to climate change for each region. The values have been discounted to 2008 prices using a discount rate of 3.5% for the 2020s and 3.0% for the 2050s

Present value of the effects of climate change (£000 s)

Regiona

Scenario

2020LO

2020HI

2050LO

2050HI

North East

−678

−43

−66

−140

North West

−57

−143

−18

−24

Yorkshire & Humberside

−1,450

−158

−274

−558

East Midlands

−846

−1,443

−872

−1,377

West Midlands

−379

−2,007

−144

−213

Eastern

−1,144

−1,538

−597

−1,114

London and South East

−525

−698

−371

−665

South West

−131

−195

−74

−140

England total

−5,210

−6,225

−2,417

−4,231

Scotland

3,647

5,680

−3,178

1,839

UK total

−1,563

−545

−5,596

−2,392

Discussion

These results, with diseases of oilseed rape in the UK, demonstrate how climate change can increase losses from crop diseases, if they are not effectively controlled. These losses have economic consequences, contributing to the vulnerability of the crop production system to climate change (Anon. 2003). In this example, the increase in losses is associated with the increase in range and severity of phoma stem canker with global warming (Butterworth et al. 2010; Evans et al. 2008). Predicted losses from this disease are substantial even though the modelling indicated that climate change may counterbalance them by decreasing losses from light leaf spot. This work illustrates how, worldwide, increased disease losses may be associated with increases in severity of existing diseases or spread of diseases to new areas to threaten crop production (Anderson et al. 2004; Chakraborty et al. 2000; Garrett et al. 2006; Gregory et al. 2009). Thus, there is a risk that the 16% of crop production lost to diseases (Oerke 2006) may increase, with serious consequences especially for the 1 billion people who do not have enough to eat (Anon. 2009; Strange and Scott 2005), unless appropriate strategies for adaptation to this effect of climate change are adopted soon.

These results emphasise the importance of including crop diseases in assessments of the adaptive capacity to climate change (Anon. 2003) of crop production systems in different areas of the world (Gregory et al. 2009). It is essential for government and industry to work together to assess risks that severity of epidemics will increase and to set priorities for control of diseases that are predicted to increase in importance, such as phoma stem canker. Whilst this work was done using fungicides to control epidemics in winter oilseed rape, it is likely that some popular fungicides will no longer be available in Europe in the future as a result of recent European Parliament legislation (Directive 91/414, http://ec.europa.eu/food/plant/protection/index_en.htm). Furthermore, many farmers in subsistence agriculture cannot afford to use fungicides. Therefore, it is essential to improve crop resistance to the pathogens that cause disease epidemics, whilst considering that some genes for resistance to pathogens become less effective as temperatures increase (Huang et al. 2006). Such improvements in resistance to diseases can contribute not only to climate change adaptation strategies but also to climate change mitigation (Mahmuti et al. 2009). One example of a government/industry partnership for improving the genetic basis of a crop so that it is more able to adapt to threats posed by climate change, including those from diseases, is the UK Oilseed RapE Genetic Improvement Network (www.oregin.net).

These predictions illustrate the contrasting impacts of climate change on different diseases and in different regions. In the UK, it is predicted that climate change will increase the severity of epidemics caused by phoma stem canker, which is favoured by increased temperature (Evans et al. 2008) but decrease the severity of epidemics caused by light leaf spot, which is favoured by cool, wet weather (Boys et al. 2007; Fitt et al. 1998; Gilles et al. 2000a). These contrasting impacts of climate change on different diseases emphasise the need for detailed assessments of the impacts of climate change on specific diseases. However, early assessments of such impacts were frequently based on qualitative reasoning that could not accommodate the complex host-pathogen-environment interactions involved (Anderson et al. 2004). It is essential to base such predictions on good long-term sets of crop, disease and weather data, that are used to construct accurate weather-based crop growth, disease severity and yield loss models. Such crop and disease models then need to be combined to provide predictions of impacts of climate change on crop growth and disease severity (Butterworth et al. 2010). Whilst there will inevitably be uncertainty in such predictions associated with uncertainty in predictions of future weather (Semenov and Stratonovitch 2010), that is no reason not to make them (Stern 2007, www.climatecongress.ku.dk) provided any assumptions made are stated.

Whilst the UK oilseed rape disease example concerns crop production in northern Europe, which may benefit from climate change (Stern 2007, www.climatecongress.ku.dk), in the context of future global food security, it will be essential to maximise food production in northern Europe. There is a need to increase crop production in northern Europe to counterbalance impacts of climate change that threaten crop production in marginal environments, such as sub-Saharan Africa (Schmidhuber and Tubiello 2007), with the consequent threats to food security in those regions (Strange and Scott 2005). A comprehensive, well-informed strategy for control of crop diseases that are likely to increase as part of a global strategy for adaptation to climate change will make an important contribution to sustainable production and global food security.

Notes

Acknowledgements

We thank the UK Biotechnology and Biological Sciences Research Council (BBSRC; Centre for Bioenergy and Climate Change ISPG) and Department for Environment, Food and Rural Affairs (Defra, OREGIN) and the Sustainable Arable LINK programme (PASSWORD, CORDISOR, CLIMDIS) and the European Commission’s Sixth Framework Programme, priority 5: ‘Food Quality and Security’ (ENDURE, 031499) for funding this research, and the British Society for Plant Pathology for supplementary funding. We also thank Marie Launay (INRA Avignon) for her STICS tutorial and help and Peter Gladders (ADAS) for the use of his data and advice.

References

  1. Anderson PK, Cunningham AA, Patel NG, Morales FJ, Epstein PR, Daszak P (2004) Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol Evol 19:535–544CrossRefPubMedGoogle Scholar
  2. Anon. (2003) The green book: annex 6. Stationery Office Books, London, p 49Google Scholar
  3. Anon. (2009) 1.02 Billion people hungry; One sixth of humanity undernourished - more than ever before. FAO (Food and Agriculture Organisation of the United Nations). http://www.fao.org/news/story/en/item/20568/icode/
  4. Biddulph JE, Fitt BDL, Leech PK, Welham SJ, Gladders P (1999) Effects of temperature and wetness duration on infection of oilseed rape leaves by ascospores of Leptosphaeria maculans (stem canker). Eur J Plant Pathol 105:769–781CrossRefGoogle Scholar
  5. Boys EF, Roques SE, Ashby AM, Evans N, Latunde-Dada AO, Thomas JE, West JS, Fitt BDL (2007) Resistance to infection by stealth: Brassica napus (winter oilseed rape) and Pyrenopeziza brassicae (light leaf spot). Eur J Plant Pathol 118:307–321CrossRefGoogle Scholar
  6. Brisson N, Gary C, Justes E, Roche R, Mary B, Ripoche D, Zimmer D, Sierra J, Bertuzzi P, Burger P, Bussiere F, Cabidoche YM, Cellier P, Debaeke P, Gaudillere JP, Henault C, Maraux F, Seguin B, Sinoquet H (2003) An overview of the crop model STICS. Eur J Agron 18:309–332CrossRefGoogle Scholar
  7. Brisson N, Ruget F, Gate P, Lorgeau J, Nicoullaud B, Tayot X, Plenet D, Jeuffroy MH, Bouthier A, Ripoche D, Mary B, Justes E (2002) STICS: a generic model for simulating crops and their water and nitrogen balances. II. Model validation for wheat and maize. Agronomie 22:69–92CrossRefGoogle Scholar
  8. Butterworth MH, Semenov MA, Barnes A, Moran D, West JS, Fitt BDL (2010) North-south divide; contrasting impacts of climate change on crop yields in Scotland and England. J R Soc Interface 7:123–130CrossRefPubMedGoogle Scholar
  9. Carleton MA (1915) A new serious wheat rust in this country. Science 42:58–59CrossRefPubMedGoogle Scholar
  10. Chakraborty S, Tiedemann AV, Teng PS (2000) Climate change: potential impact on plant diseases. Environ Pollut 108:317–326CrossRefPubMedGoogle Scholar
  11. Collins M, Tett SFB, Cooper C (2001) The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics 17:61–81CrossRefGoogle Scholar
  12. Evans N, Baierl A, Brain P, Welham SJ, Fitt BDL (2003) Spatial aspects of light leaf spot (Pyrenopeziza brassicae) epidemic development on winter oilseed rape (Brassica napus) in the United Kingdom. Phytopathology 93:657–665CrossRefPubMedGoogle Scholar
  13. Evans N, Baierl A, Semenov MA, Gladders P, Fitt BDL (2008) Range and severity of a plant disease increased by global warming. J R Soc Interface 5:525–531CrossRefPubMedGoogle Scholar
  14. Fitt BDL, Doughty KJ, Gladders P, Steed JM, Sutherland KG (1998) Diagnosis of light leaf spot (Pyrenopeziza brassicae) on winter oilseed rape (Brassica napus) in the UK. Ann Appl Biol 133:155–166CrossRefGoogle Scholar
  15. Fitt BDL, Brun H, Barbetti MJ, Rimmer SR (2006) World-wide importance of phoma stem canker (Leptosphaeria maculans and L. biglobosa) on oilseed rape (Brassica napus). Eur J Plant Pathol 114:3–15CrossRefGoogle Scholar
  16. Fitt BDL, Hu BC, Li ZQ, Liu SY, Lange RM, Kharbanda PD, Butterworth MH, White RP (2008) Strategies to prevent spread of Leptosphaeria maculans (phoma stem canker) onto oilseed rape crops in China; costs and benefits. Plant Pathol 57:652–664CrossRefGoogle Scholar
  17. Garrett KA, Dendy SP, Frank EE, Rouse MN, Travers SE (2006) Climate change effects on plant disease: genomes to ecosystems. Annu Rev Phytopathol 44:489–509CrossRefPubMedGoogle Scholar
  18. Gilles T, Evans N, Fitt BDL, Jeger MJ (2000a) Epidemiology in relation to methods for forecasting light leaf spot (Pyrenopeziza brassicae) severity on winter oilseed rape (Brassica napus) in the UK. Eur J Plant Pathol 106:593–605CrossRefGoogle Scholar
  19. Gilles T, Fitt BDL, Kennedy R, Welham SJ, Jeger MJ (2000b) Effects of temperature and wetness duration on conidial infection, latent period and asexual sporulation of Pyrenopeziza brassicae on leaves of oilseed rape. Plant Pathol 49:498–508CrossRefGoogle Scholar
  20. Gilles T, Fitt BDL, Jeger MJ (2001a) Effects of environmental factors on development of Pyrenopeziza brassicae (light leaf spot) apothecia on oilseed rape debris. Phytopathology 91:392–398CrossRefPubMedGoogle Scholar
  21. Gilles T, Fitt BDL, McCartney HA, Papastamati K, Steed JM (2001b) The roles of ascospores and conidia of Pyrenopeziza brassicae in light leaf spot epidemics on winter oilseed rape (Brassica napus) in the UK. Ann Appl Biol 138:141–152CrossRefGoogle Scholar
  22. Gregory PJ, Johnson SN, Newton AC, Ingram JS (2009) Integrating pests and pathogens into the climate change/food security debate. J Exp Bot 60:2827–2838CrossRefPubMedGoogle Scholar
  23. Huang YJ, Evans N, Li ZQ, Eckert M, Chevre AM, Renard M, Fitt BDL (2006) Temperature and leaf wetness duration affect phenotypic expression of Rlm6-mediated resistance to Leptosphaeria maculans in Brassica napus. New Phytol 170:129–141CrossRefPubMedGoogle Scholar
  24. Huang YJ, Liu Z, West JS, Todd AD, Hall AM, Fitt BDL (2007) Effects of temperature and rainfall on date of release of ascospores of Leptosphaeria maculans (phoma stem canker) from winter oilseed rape (Brassica napus) debris in the U.K. Ann Appl Biol 151:99–111CrossRefGoogle Scholar
  25. Mahmuti M, West JS, Watts J, Gladders P, Fitt BDL (2009) Controlling crop disease contributes to both food security and climate change mitigation. Int J Agric Sustain 7:189–202Google Scholar
  26. Nakicenovic N (2000) Greenhouse gas emissions scenarios. Technol Forecast Social Change 65:149–166CrossRefGoogle Scholar
  27. Oerke EC (2006) Crop losses to pests. J Agric Sci 144:31–43CrossRefGoogle Scholar
  28. Pinstrup-Andersen P (2009) Food security: definition and measurement. Food Security 1:5–7CrossRefGoogle Scholar
  29. Schmidhuber J, Tubiello FN (2007) Global food security under climate change. Proc Natl Acad Sci USA 104:19703–19708CrossRefPubMedGoogle Scholar
  30. Semenov MA (2007) Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agric For Meteorol 144:127–138CrossRefGoogle Scholar
  31. Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change 35:397–414CrossRefGoogle Scholar
  32. Semenov MA, Stratonovitch P (2010) The use of multi-model ensembles from global climate models for impact assessments of climate change. Climate Res 41:1–14CrossRefGoogle Scholar
  33. Stern N (2007) The economics of climate change: the Stern review. Cambridge University Press, UKGoogle Scholar
  34. Strange RN, Scott PR (2005) Plant disease: a threat to global food security. Annu Rev Phytopathol 43:83–116CrossRefPubMedGoogle Scholar
  35. Stukenbrock EH, McDonald BA (2008) The origins of plant pathogens in agro-ecosystems. Annu Rev Phytopathol 46:75–100CrossRefPubMedGoogle Scholar
  36. Su H, Fitt BDL, Welham SJ, Sansford CE, Sutherland KG (1998) Effects of light leaf spot (Pyrenopeziza brassicae) on yield of winter oilseed rape (Brassica napus). Ann Appl Biol 132:371–386CrossRefGoogle Scholar
  37. Sylvester-Bradley R, Makepeace RJ (1985) Revision of a code for stages of development in oilseed rape (Brassica napus L.). Aspects of Applied Biology. Field Trials Methods Data Handling 10:395–400Google Scholar
  38. Welham SJ, Turner JA, Gladders P, Fitt BDL, Evans N, Baierl A (2004) Predicting light leaf spot (Pyrenopeziza brassicae) risk on winter oilseed rape (Brassica napus) in England and Wales, using survey, weather and crop information. Plant Pathol 53:713–724CrossRefGoogle Scholar
  39. West JS, Kharbanda PD, Barbetti MJ, Fitt BDL (2001) Epidemiology and management of Leptosphaeria maculans (phoma stem canker) on oilseed rape in Australia, Canada and Europe. Plant Pathol 50:10–27CrossRefGoogle Scholar
  40. Zhou Y, Fitt BDL, Welham SJ, Gladders P, Sansford CE, West JS (1999) Effects of severity and timing of stem canker (Leptosphaeria maculans) symptoms on yield of winter oilseed rape (Brassica napus) in the UK. Eur J Plant Pathol 105:715–728CrossRefGoogle Scholar
  41. Zhou Y, Fitt BDL, Welham SJ, Evans N, Gladders P (2000) Effects of stem canker (Leptosphaeria maculans) and light leaf spot (Pyrenopeziza brassicae) on yield of winter oilseed rape (Brassica napus) in southern England. Plant Pathol 49:487–449CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media B.V. & International Society for Plant Pathology 2010

Authors and Affiliations

  • Neal Evans
    • 1
  • Michael H. Butterworth
    • 1
  • Andreas Baierl
    • 2
  • Mikhail A. Semenov
    • 1
  • Jon S. West
    • 1
  • Andrew Barnes
    • 3
  • Dominic Moran
    • 3
  • Bruce D. L. Fitt
    • 1
  1. 1.Rothamsted ResearchHarpendenUK
  2. 2.Department of Statistics and Decision Support SystemsUniversity of ViennaViennaAustria
  3. 3.Scottish Agricultural CollegeEdinburghUK

Personalised recommendations