Int J Biometeorol DOI 10.1007/s00484-014-0808-6

ORIGINAL PAPER

Modelling soil borne fungal pathogens of arable crops under climate change L. M. Manici & S. Bregaglio & D. Fumagalli & M. Donatelli

Received: 5 September 2013 / Revised: 6 February 2014 / Accepted: 18 February 2014 # ISB 2014

Abstract Soil-borne fungal plant pathogens, agents of crown and root rot, are seldom considered in studies on climate change and agriculture due both to the complexity of the soil system and to the incomplete knowledge of their response to environmental drivers. A controlled chamber set of experiments was carried out to quantify the response of six soilborne fungi to temperature, and a species-generic model to simulate their response was developed. The model was linked to a soil temperature model inclusive of components able to simulate soil water content also as resulting from crop water uptake. Pathogen relative growth was simulated over Europe using the IPCC A1B emission scenario derived from the Hadley-CM3 global climate model. Climate scenarios of soil temperature in 2020 and 2030 were compared to the baseline centred in the year 2000. The general trend of the response of soil-borne pathogens shows increasing growth in the coldest areas of Europe; however, a larger rate of increase is shown from 2020 to 2030 compared to that of 2000 to 2020. Projections of pathogens of winter cereals indicate a marked increase of growth rate in the soils of northern European and Baltic states. Fungal pathogens of spring sowing crops show unchanged conditions for their growth in soils of the L. M. Manici (*) : M. Donatelli Consiglio Nazionale per la Ricerca e sperimentazione in Agricoltura, Research Centre for Industrial Crops, via di Corticella, 133, 40128 Bologna, Italy e-mail: [email protected] S. Bregaglio Department of Agricultural and Environmental Sciences Production, Landscape, Agroenergy - CASSANDRA, University of Milan, via Celoria, 220133 Milan, Italy D. Fumagalli European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, MARS Unit, AGRI4CAST Action, via Fermi 2749-TP483, 21027 Ispra, Italy

Mediterranean countries, whereas an increase of suitable conditions was estimated for the areals of central Europe which represent the coldest limit areas where the host crops are currently grown. Differences across fungal species are shown, indicating that crop-specific analyses should be ran. Keywords Modelling crop diseases . Large area simulation . Foot rot . Winter cereals . Spring sowing crops . Soil temperature

Introduction The need to anticipate and respond to the effects of climate change on agriculture presents many challenges. The fundamental one is to understand how climate change will influence agricultural and natural ecosystems as well as their services (Millennium Ecosystem Assessment MEA 2005). The projections of future climate impacts on agriculture have mainly been based on the impact of changing meteorological conditions on crop physiology (Rao et al. 2011; Tao et al. 2006). Most of the available studies aim at quantifying yield fluctuations due to different thermal conditions and CO2 concentration values affecting plants (e.g. Porter and Semenov 2005; Challinor et al. 2010). One emerging aspect concerns the assessment of the future dynamics of pest and pathogens of plants, which are key determinants both of crop yield levels and of natural environments (Anderson et al. 2004; Loo 2009). Projections of the future impact of air-borne fungal pathogens on crops can be estimated based on meteorological variables (e.g. air temperature, vapour pressure deficit, leaf wetness duration) and can be considered based on their interaction with plant biophysical processes (e.g. leaf area evolution, biomass accumulation, host susceptibility; Coakley et al. 1999; Bregaglio et al. 2013). On the contrary, the great complexity of the soil system adds a layer of substantial

Int J Biometeorol

complexity to the forecasting of the impact of soil-borne pathogens on plants (Gilligan 1983; Hersh et al. 2012). Furthermore, there is an evidence that their responses to different environmental conditions could be very heterogeneous, thus potentially increasing their harmfulness (Broders et al. 2009). This leads to considering soil-borne fungal pathogens as one component in the complexity of crop-climateenvironment interactions, which makes projecting the net outcome of climate change in agriculture difficult; for example, the last USDA report on climate change and agriculture in the USA (Walthall et al. 2012). Soil-borne fungal pathogens are the causal agents of root rot in herbaceous and fruit tree crops and represent the main biotic components of yield decline in intensively cultivated areas. Their specific impact on yield losses is not easy to evaluate, given the difficulties in distinguishing their role from one of the scarce soil fertility or abiotic stresses; moreover, soil-borne pathogens can reduce crop quality in both direct and indirect ways (Dixon and Tilston 2010; Schwartz 2012). The epidemiology of soil-borne pathogens is affected by several biological and physiochemical factors (Bockus and Shroyer 1998). They can establish symbiotic relationships with plants, having an impact ranging in severity from decreased growth rates to plant death, depending on host susceptibility and/or on environmental conditions (Redman et al. 2001). Given their saprophytic behaviour, soil-borne pathogens can expand in soil on organic residues (Bockus and Shroyer 1998) and in most cases vegetative hyphae can directly penetrate plant cells. This permits exploration, via pure cultures, of their response to variable pH, nutrients, water availability, chemical active principles and temperature. (ElHissy and Abdel-Kader 1980; Marín et al. 1995; Pettitt et al. 1996; Kim et al. 2005). Soil temperature is the main driving force of the development of soil-borne pathogens and consequently the key physiochemical factor of their ecology, as water availability is often non-limiting to their growth in the presence of crops. However, an accurate estimation of soil temperature requires an explicit and reliable simulation of soil water content, which markedly impacts on heat transfer in soils (Lakshmi et al. 2003; Donatelli et al. 2014). According to the Intergovernmental Panel on Climate Change (IPCC 2007), forecasts for the twenty-first century indicate a generalized increase of air temperature and an intensification of the frequency of extreme events. Such changes are likely to impact on soil temperature regimes, potentially causing an increase of biological pressure of soil-borne pathogens on crops; in addition, they might experience shifts in their target host range, thus increasing the number of crops affected by their damage. The objective of this paper was to develop and apply a large-scale simulation system to estimate the response of soilborne pathogens to soil temperature variation under a climate change scenario, covering Europe and targeting short to

midterm time horizons, which represent those which are of most interest to set adaptation strategies to climate change in agriculture.

Materials and methods The study was carried out in three phases: (i) the establishment of controlled chamber sets of experiments to quantify the response of soil-borne pathogens to temperature; (ii) the development of a generic model to simulate their growth and the implementation of a modelling solution to simulate soil temperature; (iii) the application of this spatialized simulation system runs to test pathogen responses to soil temperature over Europe, comparing future scenarios to a baseline weather. The response of soil-borne pathogens to temperature Six soil-borne fungal pathogens were selected according to different temperature requirements and to their known economic impact on crop yield; they were taken as representative of the complexes of root pathogens of autumn and spring sowing crops. Three of them are fungal pathogen agents of the foot rot pathosystem of winter cereals, representative of fall crops: &

&

&

Fusarium nivale (Microdochium nivale) is the dominant agent of early foot rot or fusarium patch of winter cereals and grasses worldwide (Smiley and Patterson 1996; Colbach et al. 1996). Among the Fusarium species affecting cereals, it is favoured by the coolest temperatures, ranging from 10 to 15 °C (Millar and Colhoun 1969); Fusarium culmorum is the dominant member of Fusarium crown rot of cereals in non-irrigated areas and temperate semiarid climates (Cook 1980; Smiley and Patterson 1996; Backhouse and Burgess 2002). It is favoured by temperatures ranging from 15 to 26 °C (Palmero et al. 2009); Bipolaris sorokiniana (imperfect stage of Cochliobolus sativus) causes common root rot on wheat and barley and is present worldwide in major cereal growing regions. Warm soil temperatures favour its growth, and attacks occur between 16 and 40 °C, with optimal soil temperatures ranging from 28 to 32 °C (Mathre 1997; Mehta 1998).

The other three fungal pathogens representing the complex of fungal agents of root and stem rot of herbaceous spring sowing crops (e.g. sunflower, soybean, sorghum) are the following: &

Pythium ultimum is the main agent of seedling disease complex at early crop stages; it is favoured by conditions

Int J Biometeorol

&

&

of low temperature and high-soil moisture at sowing time. It can attack a large range of crops at seedling stage (Hendrix and Campbell 1973). Sclerotinia minor is the agent of crown rots on several herbaceous crops (e.g. sunflower), white mould of horticultural crops and lettuce drop. Its infection is promoted by wet and relatively cold temperatures (12–24 °C) (Heffer and Johnson 2007). Macrophomina phaseolina is a pathogen of subtropical origin, also adaptive to temperate agricultural regions. Its optimal mycelia growth is around 30–35 °C (Manici et al. 1995). It is favoured by water-stressed conditions and can attack about 500 plant species causing seedling blight, root rot and charcoal rot. Sunflower, sorghum, maize, cotton and soybean are the main host crops of this pathogen (Dhingra and Sinclair 1978; Jimenez Diaz et al. 1983).

Each pathogen of these two complexes can have a dominant or codominant role depending on the climate conditions occurring in the growing season. Pure colonies of each pathogen were taken from culture collection of CRA-CIN (http://www.cra-cin.it/isciTC/ Micoteca_web/index.html; they were included with the following codes: F_niv04, Pyt_ult34, F_culm05, Scl_min01, Hel_sat01, Mac160). Three Petri plates (diameter 90 mm) containing 10 mL of potato sucrose agar, a conventional growing media for filamentous fungi, were inoculated in the centre with a 4-mm diameter agar disk taken from each pure colony and grown for 5–6 days after regeneration for each treatment. Plates were incubated at different temperatures ranging from 1 to 45 °C, according to the temperature requirements of the pathogens, in a Binder KBW 400 growth chamber (from 8 to 45 °C) and in a ventilated cooled chamber (from 1 to 9 °C). For each testing temperature, the minor and major radii of each colony were measured at the beginning and at the end of each working day for temperatures close to the expected optimum, and daily for other temperatures, until all colonies reached the edge of the Petri plate or the seventh day of incubation was reached. Response was expressed as growth rate (mm day−1), resulting as the average of the daily increase of the radii. The first cycle of controlled chamber experiments was carried out from January to April 2011; the second cycle was carried out from January to May 2012, whereas the third additional cycle at three temperatures was carried out for S. minor, which showed a higher variability than other fungi of the growth rate response to temperature. Modelling the response of soil-borne pathogens to temperature Growth rates were normalized by dividing the observed response to a given temperature by the maximum growth rate measured for a given pathogen in each growth chamber

experiment. This approximation (i.e. slightly higher maximum rates could have been measured at the real optimum temperature, possibly not tested) was needed to normalize the data. Linear growth of hyphae differs across species, hence this normalization allowed comparing different responses to temperature using an equation with parameters having a biological meaning. Growth rates were hence expressed as 0–1 values, with 1 corresponding to the response to optimal temperature for growth. The function proposed by Yan and Hunt (1999) was used to fit soil-borne pathogen growth response to temperature. The resulting curve is based on the three cardinal temperatures: Tmin (°C), minimum temperature for growth (below this temperature there is no observable growth), Topt (°C), optimum temperature for growth and Tmax (°C), maximum temperature for growth (above this temperature, the growth ceases). The function is
f ðt Þ ¼

T max −T T max −T opt




 T −T min ðT opt −T min ÞÞ%=ðT max −T opt Þ ð1Þ T opt −T min

where T (°C) is the testing temperature. The parameters corresponding to the cardinal temperatures of the tested pathogens were optimized using the Microsoft Excel Solver, using as objective function the minimization of the sum of squares of differences estimated-observed growth rates. In order to avoid incoherencies due to mere fitting iterations, the constraint imposed on the optimization algorithm was setting Tmin to the known value from literature. This prevented obtaining biologically unacceptable low results without such limit, with a negligible gain in minimizing the error (the curve used would have approached asymptotically Tmin). This hourly time-step function was implemented as a software component adopting the architecture presented by Donatelli and Rizzoli (2008). The weather scenarios In order to estimate potential growth of soil-borne pathogens in the future, scenarios of climate change centred in 2020 and 2030 were used. The daily input weather data were derived from the bias-corrected ENSEMBLES dataset (Dosio and Paruolo 2011) at a spatial resolution of 25×25 km, by downscaling the realizations of the IPCC A1B emission scenario given by the global circulation model HadCM3 nested with the regional circulation model HadRM3 (Donatelli et al. 2012a, b). Such scenarios were compared to the baseline, representing a sample of 30 years of daily weather, centred in the year 2000. A spatialized simulation experiment was carried out in the EU27 territory according to the geographical distribution of winter wheat and sunflower (MARS database,

Int J Biometeorol

Micale and Genovese 2004), chosen to simulate the dynamics of fall and spring sowing crops water uptake.

Table 1 reports the equations adopted in this study for the simulation of soil temperature along the soil profile.

The modelling solution

Results

Since soil temperature in the first 0.1 m is a crucial driving force for soil-borne pathogens development, a modelling solution taking account of the effects of crop growth and water dynamics along the soil profile was implemented. A unique, synthetic soil profile was used for simulation, representing a loam type soil. It was arbitrarily chosen as representative of soil thermal dynamics due to the uncertainty in characterizing actual pedological profiles for the whole covered area. Also, being near-surface soil more sensitive to above ground temperature, hourly values of soil temperature, at reference depths of 0.05 and 0.1 m were given as input to the soil-borne pathogens component to simulate the relative growth rates of the tested fungi. In order to estimate crop water uptake, the CropSyst model (Stöckle et al. 2003) was chosen, which proved to be suitable for large area assessments (Confalonieri et al. 2010). In addition, parameterizations for many species are available in a wide range of pedoclimatic conditions (e.g. Donatelli et al. 1997; Jara and Stöckle 1999; Bechini et al. 2006). CropSyst was ran under water-limited conditions and hence parameterized for wheat and sunflower. The simulation of water dynamics along the soil profile was performed by adopting the “cascading” approach, which assumes that water can move only downward through the soil profile, filling up the layers until field capacity is reached (Jones and Ritchie 1990; Ritchie 1998). This simplified approach was chosen because it requires only texture data as input, thus being a needed choice for large area simulation where the availability of accurate soil data resulted limiting. For the simulation of soil surface temperature and daily soil temperature along the profile, the models developed by Parton (1984) and the one implemented in the SWAT model (Neitsch et al. 2002) were coupled and applied, which proved to be more accurate compared to other modelling solution (Donatelli et al. 2013). In order to limit errors in the estimation of development rates of fungal pathogens derived by the use of daily average (Horton 2012; Vogt and Bedo 2001), an hourly time-step temperature simulation was carried out. The daily range of soil temperature at different depths was computed according to the DAYCENT model (Parton et al. 1998). Its calculation was performed starting from soil thermal conductivity, which was estimated by the pedotransfer function developed by Bristow (2002) as implemented in the SIMULAT model (Diekkrüger et al. 1995). Soil thermal capacity was estimated as a function of soil water content and of the heat capacities of soil constituents (Kluitenberg 2002). Hourly soil temperature simulation was then carried out according to Parton and Logan (1981).

Calibration of the temperature requirements of soil-borne pathogens Relative growth rates versus temperature response of the six soil-borne pathogens are shown together with the calibrated functions in Fig. 1. The estimated parameter values are reported in Table 2. Observed and modelled normalized growth rates of the six soil-borne pathogens tested show the different temperature requirements of the three reference soil-borne pathogens within each complex. In whole, in vitro growth rate findings match what is known of their thermal requirements. F. nivale was confirmed to be the root rot agent of cereals most suited to the coldest temperatures and B. sorokiniana, the more favoured by the warmest ones. Accordingly, despite having diverse optimal temperatures, P. ultimum and S. minor, having Tmin =0, proved to be suited to the cold conditions. Finally, M. phaseolina was confirmed to be the only strictly thermophilous pathogen within the fungal complex of spring sowing crops (Fig. 1; Table 2). Simulation of soil temperature in climate change scenarios The difference of daily average soil temperature in the 0.1 m top soil layer resulting from simulations is shown in Fig. 2. Maps present the differences for 2020 versus 2000 and 2030 versus 2000 time frames in the two 6-month periods (October–March and April–September). Overall, the largest soil temperature increase is observed over the winter period, more markedly in central Europe and northeastern countries. The increase of soil temperature in western Europe is decidedly limited (−1÷+1 °C) in the 2020 time horizon both for the summer and winter periods. Spain shows a larger increase of soil temperature compared to Italy and Greece during the summer months in the 2030 time frame. Cultivated areas of the UK and Scandinavian countries are not included in these maps, but they were considered in the simulation of foot rot pathogens of winter cereals. Response of soil-borne pathogens to climate change scenarios Data of pathogen response to climate change scenarios are presented in maps as a percentage difference of cumulative relative growth comparing the future scenarios to the reference weather (baseline centred in 2000). Positive values indicate an increased potential growth.

Int J Biometeorol Table 1 Equations used for estimating hourly temperature along the soil profile Variable Txs Tns Ts

Rd Tcon

Tcap Tdiff Tsh

Equation

Reference

  T xs ¼ T xa þ ½24ð1−e−0:038⋅S r Þ þ 0:35T xa Š e−4:8⋅AGB −0:13 Tns =Tna6AGB−1.82     T soil ðz; d n Þ ¼ lT soil ðz; d n −1Þ þ ð1−l Þ df T air −T s þ T s where zd df ¼ zd þexpð−0:867−2:078⋅z zd ¼ ddz dÞ   2 1−ϕ 500 2500ρb dd ¼ ddmax e lnðddmax Þ⋅ð1þϕÞ ddmax ¼ 1000 þ ð−5:63ρb Þ ρb þ686e

θ φ ¼ ð0:356−0:144ρ

 b Þztot 0:5 

Rd ¼ Rs e

−z⋅

Parton 1984 Parton 1984 Neitsch et al. 2002

Parton et al. 1998; Bristow 2002

0:00005 T diff

T con ¼ A þ Bθ−ðA−DÞeð−ðCθÞ Þ where A=0.65−0.78ρb +0.6ρb2 B=1.06ρbθ pffiffiffiffiffiffi C ¼ 1 þ 2:6 mc D=0.03+0.1ρb2 Tcap =ρbψmcm +ρbψoco +cwθ

Kluitenberg 2002

T diff ¼ TT con cap Daylight hours

Parton and Logan 1981

T sh ¼ ðT xs −T ns Þsin

4



πm yþ2a



þ T ns

Night-time hours bn T sh ¼ T ns þ ðT sun −T ns Þeð− x Þ

Txs = maximum daily surface soil temperature (°C), Txa = maximum daily air temperature (°C), Sr = daily solar radiation (MJ m−2 day−1 ), AGB = plant biomass (kg m−2 day−1 ) Tns = minimum daily surface soil temperature (°C), Tna = maximum daily air temperature (°C), Tsoil = soil temperature among the layers (°C), z = soil layer depth (mm), l = lag coefficient (unitless), df = depth factor quantifying influence of depth on soil temperature (mm), T air = average annual air temperature (°C), Ts = average daily surface soil temperature (°C), zd =ratio of depth at the center of soil layer to the dumping depth (unitless), dd = actual dumping depth (mm), ddmax = maximum dumping depth (mm), ϕ = scaling factor for water content (unitless), ρb = soil bulk density (t m−3 ), φ = volumetric water content (m3 m−3 ), ztot = soil bottom depth (mm), Rd = daily range of soil temperature (°C), Rs = daily range of soil surface temperature (°C), Tdiff = soil thermal diffusivity (mm s−1 ), Tcon = soil thermal conductivity (W m−1 K−1 ), mc = clay mass fraction (unitless), Tcap = soil thermal capacity (MJ m−3 K−1 ), Ψm = mass fraction of sand, clay and silt (unitless); cm = specific heat of soil (0.73 kj kg−1 K−1 ), Ψo = mass fraction of organic matter (unitless), co = specific heat of organic matter (1.9 kj kg−1 K−1 ), cw = specific heat of water (4.18 kj kg−1 K−1 ), Tsh = hourly soil temperature (°C), m = number of hours between time of Tns and sunset (h), y = day length (h), a = lag coefficient for Txs (1.8 h), Tsun = temperature at sunset (°C), b = night-time temperature coefficient (unitless, 2.20), n = number of hours from sunset to the time of Tns (h), x = night length (h)

Differences of average soil temperature estimated as 1 °C must be considered as meaningful, because of their cumulative effects over the whole period of analysis (six either winter or summer months), hence potentially yielding a significant response for the continuous growth model used. Response of soil-borne pathogens of fall sowing crops Simulations across the winter period (October–March) of the growth rate of the three soil-borne pathogens of winter cereals showed an overall increase in the future scenarios compared to current conditions (Fig. 3), with the highest peaks of differences in the coldest agricultural areas of northern and eastern Europe (e.g. Scandinavian and Baltic countries). The simulations of F. nivale on winter wheat-growing areas (as a proxy for fall sowing crops) showed an increase of more than 35 % in the growth rate in Scandinavian and Baltic states with respect to the baseline. By contrast, its increase did not exceed 15 % even in the worst projections (2030) in southern Europe (Iberia, the Italian and Balkan Peninsulas) and France,

except for some small mountainous or continental agricultural areas (Fig. 3). The results suggest that F. nivale growth rate markedly increases in the area with the highest soil temperature increase according to the simulation of soil temperature scenarios in the winter period (Fig. 2). These projections find consistence with the correlation observed by Pettitt et al. (1996) between the temperature requirements inferred from in vitro experiments and the observed outbreaks caused by foot rot on winter cereals occurring in Scotland in correspondence to increased soil temperatures. Spatialized simulations in the 2020 time frame of F. culmorum and B. sorokinina presented an overall similar pattern. They agree in depicting a high increase of differences in growth rate (>25 %) in Denmark, Scandinavian and Baltic countries and in some cold continental areas of eastern Europe. Conversely, simulations showed an alternating slightly decreasing and increasing growth rate in different areas of Mediterranean and central Europe (Fig. 3). The projections of fungal growth rate in soil for these two cereal pathogens showed a variable increase (+5÷+35 %) in Mediterranean

Int J Biometeorol

Fig. 1 Observed and modelled normalized growth rates of the six soil-borne pathogens tested, F. nivale, F. culmorum, B. sorokiniana, P. ultimum, S. minor and M. phaseolina. Different symbols correspond to different cycles of growth rate assessments in growth chamber

countries, France and in some agricultural areas of Germany and southeastern Europe in 2030. Peaks of differences in the relative growth rate above 35 % are expected in the UK. These two pathogens are characterized by higher temperature requirements than F. nivale (Tmin 5.7 for F. culmorum and 4 °C for B. sorokiniana). This might explain why their projections

indicate a generalized higher growth rate in the continental areas of Mediterranean and central Europe, with a marked homogeneity in Scandinavian, eastern Europe and Baltic countries in 2030, where soil temperature estimates are the largest given by climate models (Fig. 2). Response of soil-borne pathogens of spring sowing crops

Table 2 Parameters calibrated for the six soil-borne pathogens. Tmin, minimum temperature for development (°C), Topt, optimum temperature for development, Tmax, maximum temperature for development (°C) Pathogen

Tmin

Topt

Tmax

Fusarium culmorum Fusarium nivale Bipolaris sorokiniana Pythium ultimum Sclerotinia minor Macrophomina phaseolina

5.7 0.0 4.0 0.0 0.0 6.0

23.4 18.6 30.8 27.1 22.3 32.9

33.7 28.1 39.6 36.5 29.6 41.0

The pathogens studied can attack spring sowing crops at different growth phases. P. ultimum and S. minor occur at early and mid-growth stages, whereas M. phaseolina usually causes charcoal rot at the beginning of the ripening stage in the summer period, even if it colonizes roots during earlier stages. P. ultimum and S. minor responses to growth rate in future scenarios compared to current conditions are similar. Indeed, they showed an overall unchanged pressure in Mediterranean countries, whereas their growth rate was projected to increase in the coldest cultivated areas of central and eastern Europe (Fig. 4). Simulations in 2020 scenarios of those two pathogens

Int J Biometeorol

Fig. 2 Difference of daily average soil temperature in the 0.1-m top soil layer for 2020 versus 2000 and 2030 versus 2000 time frames in two 6-month periods (October–March and April–September) in Europe

show an unvaried growth rate in the Mediterranean and southeastern European countries, with an increase of suitable conditions in the coldest limit areas where the host crops are grown (northern regions of France, Germany and Poland). Simulations of 2030 presented a similar trend with a consistent increment in the growth rate limited to central Europe;

however, S. minor is estimated to find less favourable conditions compared to baseline in the agricultural soils of Mediterranean and southeastern countries (−15÷−5 %). This could be due to the shorter range of limiting temperatures of S minor compared to P. ultimum, specifically to its lower Tmin and Tmax calibrated values (Table 2).

Int J Biometeorol

Int J Biometeorol

ƒFig.

3 Differences of growth rates of F. nivale, F. culmorum and B. sorokiniana (agents of foot rot of wheat and cereals) in agricultural soils of Europe between the winter periods (October–March) of 2020 and 2030 versus current climatic conditions centred in 2000

The response of M. phaseolina to future scenarios showed a similar trend with respect to the other two pathogens of spring sowing crops, but with a more marked increase of growth rate in the 2030 projections. The 2020 simulations are characterized by an unchanged growth in the Mediterranean and southeastern European countries, together with an increase of suitable conditions in the northern regions of central and eastern Europe. However, the projections for M. phaseolina in the 2030 time frame (Fig. 4) showed increases of growth rate from 5 to 25 % in the Mediterranean and southeastern countries, whereas results present an increase from 25 to 35 % in the coldest limit areas where the target host crop is grown, such as the northern regions of France, Germany and Poland.

Discussion In analysing changes of soil temperature caused by climate change, a key feature to be pointed out is that this variable is, on one hand, positively correlated to air temperature, but on the other hand, a function of soil water content; the higher the soil water content, the lower the soil temperature. Hence, soil temperature is also a function of crop water uptake and consequently of the crop cycle duration; the latter shortens when temperature increases to the optimum for plants. This implies that not always a simple and linear relationship between air temperature, as driving force from global climate models, and soil borne pathogens response can be observed. Further, air temperature may reach, sometimes frequently, values above the optimum for the growth of organisms, whereas soil acts as a buffer thus determining a markedly lower temperature increase in the top layer soil compared to air temperature. In other words, when considering short to medium-term time horizons, the range of fluctuation of soil temperature tends to be still either below or close to the optimum. Consequently, in most of the conditions evaluated, the growth rate of soil organisms is rarely projected to decrease, opposite as it does for the above-ground organisms where the decline is frequent at the highest temperatures. The simulations across the winter period (October–March) of the three soil-borne fungal agents of foot rot complex of winter cereals showed an overall increase in the future scenarios compared with current conditions (Fig. 3). The spatialized simulations of the foot rot agents presented a peak of increase in the coldest areas of Europe; this could be mainly due to the consequent reduction of limiting thermal conditions during

the early growth stages of winter cereals, from germination to tillering development stages (Zadoks et al. 1974). These findings suggest that each of these pathogens will increase the possibility of becoming alternately dominant or codominant in function of the weather conditions from the seeding to the advanced tillering stages. Furthermore, temperature directly impacts on their pattern of distribution (Cook 1980; Daamen et al. 1991; Pettitt et al. 1996) and attacks of F. culmorum, B. sorokinina and F. nivale on winter wheat have been correlated with lower plant vigour and reduced yield (Sturz and Bernier 1989; Smiley and Patterson 1996). Consequently, the potential biological pressure of foot rot disease as biotic component responsible for yield decline of winter cereals is estimated to increase. Such increase will not be linear from the baseline to 2030 but is estimated to be more marked moving from the 2020 to the 2030 time horizons. The general trend of response of the soil-borne pathogens relevant to spring sowing crops is projected to increase in the coldest areas of central Europe, whereas the impact of temperature conditions in southern and southeastern Europe are estimated to remain almost unchanged in future projections for the 2020 time horizon with respect to the baseline. Whilst this trend entirely concerns P. ultimum and S. minor, M. phaseolina, a soil-borne pathogen of subtropical origin, seems to find favourable conditions increasing over time everywhere in agricultural soils of Europe. Therefore, Pythium and Sclerotinia could assume a larger impact on yield losses of spring sowing crops in the cold continental areas of central Europe; whereas M. phaseolina could shift and expand its areal of distribution towards relatively colder areas of central Europe, where it has not yet been included among pathogens threatening spring sowing crops such as sunflower or soybean. The latter finding is consistent with the expansion of M. phaseolina to the northern states in the USA since the early 2000s. Indeed, this pathogen was found to be the main cause of yield decline of sunflower and soybean in South and North Dakota, Minnesota and Iowa, characterized by a humid continental climate and very different from the typical habitat of this fungus (Dhingra and Sinclair 1978). M. phaseolina impact on crop yield production had been substantially negligible in those states in the past, where previously it had only occasionally been isolated (Gulya et al. 2002; El Araby et al. 2003; Yang and Navi 2005). In addition, M. phaseolina, which impacted on arable crops such as soybean, cotton, sunflower and sorghum in the 1980s and 1990s (Dhingra and Sinclair 1978; Jimenez Diaz et al. 1983; Wrather et al. 1995) has been reported to cause losses on several horticultural and high-value crops such as strawberry, melon and vine, in the last decade (Aegerter et al. 2000; Zveibil and Freeman 2005; Avilés et al. 2008). The general trend of soil-borne pathogen response to future weather scenarios, as outlined by this study, clearly shows a marked increase of their potential relative growth rate in the

Int J Biometeorol

Int J Biometeorol

ƒFig.

4 Differences of growth rate of P. ultimum, S. minor and M. phaseolina (agents of root and crown rot of sunflower and several other arable crops) in agricultural soils of Europe between the summer periods (April–September) of 2020 and 2030 versus current climatic conditions centred in 2000

colder areas of Europe, but with some differences which should be investigated with more detailed information on the soils and exposure. This may cause an increased susceptibility of winter cereals to foot rot and consequently a reduced stability of grain production over the years in the coldest agricultural lands of northern and eastern Europe. Crop rotation is the principal means for controlling soil-borne pathogens both in conventional and integrated production systems (Glynne 1965; LaMondia et al. 2002; Juroszek and von Tiedemann 2011); biophysical models able to predict the impact on crops of soil-borne pathogens should be considered by the European Commission when planning agricultural policies and guidelines for adaptation strategies to climate change in agriculture. This appears of particular importance for strategies concerning Integrated Crop Production, as this modelling solution for soil-borne pathogens simulation could represent useful support when adopting European Commission directives in the national Integrated Production guidelines by the EU-27 member states (European Commission DG ENV 2002; Boller et al. 2004). Although the approach adopted in this study does not include the simulation of actual yield levels and quality decline of the crops, it opens further perspectives for the application of biophysical models in predicting response to climate change for a large number of soil fungal pathogens, either of agricultural or forestry interest. The modelling solution can be further developed to match in greater detail the phenology of specific hosts with respect to their sensitivity to pathogens. This would allow more specific estimates of the potential damage in future scenarios, thus reproducing with increased accuracy the complex interactions of the specific pathosystems. Such modelling solutions could be easily imported into the BioMA platform (Donatelli et al. 2012a, b allowing an easy rerun of the spatialized simulations with different pathogens, and by including other key aspects of the agricultural systems such as estimates of impact on crops. Also, the parameterization of soils could be specialized both as soil type and as soil slope and aspect (i.e. exposure orientation). The simulation system described in this study is a step forward in the analysis of production systems where there is a lack of adaptation of crops, hence allowing a more integrated evaluation of system performance in the future.

Acknowledgments Study funded by the project AgroScenari of the Italian Ministry of Agriculture, Food and Forestry Policies.

References Aegerter BJ, Gordon TR, Davis RM (2000) Occurrence and pathogenicity of fungi associated with melon root rot and vine decline in California. Plant Dis 84:224–230, 10.1094/PDIS.2000.84.3.224 Anderson KA, Cunningham AA, Patel NG, Morales FJ, Epstein PR, Daszakc P (2004) Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol Evol 19:535–544, 10.1016/j.tree.2004.07.021 Avilés M, Castillo S, Bascon J, Zea-Bonilla T, Martín-Sánchez PM, Pérez-Jiménez RM (2008) First report of Macrophomina phaseolina causing crown and root rot of strawberry in Spain. Plant Pathol 57: 382. doi:10.1111/j.1365-3059.2007.01717.x Backhouse D, Burgess LW (2002) Climatic analysis of the distribution of Fusarium graminearum, F. pseudograminearum and F. culmorum on cereals in Australia. Aust Plant Path 31:321–327. doi:10.1071/ AP02026 Bechini L, Bocchi S, Maggiore T, Confalonieri R (2006) Parameterization of a crop growth and development simulation model at sub-model components level. An example for winter wheat (Triticum aestivum L.). Environ Model Softw 21:1042–1054. doi: 10.1016/j.envsoft.2005.05.006 Bockus W, Shroyer J (1998) The impact of reduced tillage on soilborne plant pathogens. Annu Rev Plant Physiol Plant Mol Biol 36:485– 500. doi:10.1146/annurev.phyto.36.1.485 Boller EF, Avilla J, Joerg E, Malavolta C, Wijnands FG, Esbjerg P (2004) Integrated production principles and technical guidelines. In: Boller EF, Avilla J, Joerg E, Malavolta C, Wijnands FG, Esbjerg P (eds) 3rd edn. IOBC wprs Bulletin Bulletin OILB. 27:(2)2004, http://www. iobc-wprs.org/ip_ipm/01_IOBC_Principles_and_Tech_ Guidelines_2004.pdf Accessed 13 August 2013 Bregaglio S, Donatelli M, Confalonieri R (2013) Fungal infections of rice, wheat, and grape in Europe in 2030–2050. Agron Sust Devel 33:767–776. doi:1007/s13593-013-0149-6 Bristow KL (2002) Thermal conductivity. In: Methods of soil analysis. Part 4. Physical methods. In: Dane JH, Topp GC (eds). Madison, pp. 1209–1226 Broders KD, Wallhead MW, Austin GD, Lipps PE, Paul PA, Mullen RW, Dorrance AE (2009) Association of soil chemical and physical properties with Pythium species diversity, community composition, and disease incidence. Phytopathology 99:957–967. doi:10.1094/ PHYTO-99-8-0957 Challinor AJ, Simelton ES, Fraser EDG, Hemming D, Collins M (2010) Increased crop failure due to climate change: assessing adaptation options using models and socio-economic data for wheat in China. Environ Res Lett 5:034012. doi:10.1088/1748-9326/5/3/034012 Coakley SM, Scherm H, Chakraborty S (1999) Climate change and plant disease management. Annu Rev Plant Physiol Plant Mol Biol 37: 399–426. doi:10.1146/annurev.phyto.37.1.399 Colbach N, Maurin N, Huet P (1996) Influence of cropping system on foot rot of winter wheat in France. Crop Prot 15:295–305. doi:10. 1016/0261-2194(95)00150-6 Confalonieri R, Bregaglio S, Acutis M (2010) A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions. Ecol Modell 221: 960–964. doi:10.1016/j.ecolmodel.2009.12.003 Cook RJ (1980) Fusarium foot rot wheat and its control in the Pacific Northwest. Plant Dis 64:1061–1066 Daamen RA, Langerak CJ, Stol W (1991) Surveys of cereal diseases and pests in the Netherlands. 3. Monographella nivalis and Fusarium spp. in winter wheat fields and seed lots. Neth J Plant Path 97:105– 114. doi:10.1007/BF01974274 Dhingra OD, Sinclair JB (1978) Biology and pathology of Macrophomina phaseolina. Imprensa da Universidade Federal de Viscosa, Brazil

Int J Biometeorol Diekkrüger B, Nöersheuser P, Richter O (1995) Modelling pesticide dynamics of a loam site using HERBSIM and SIMULAT. Ecol Modell 81:111–119. 10.1016/0304-3800(94)00164-D Dixon GR, Tilston EL (2010) Soil-borne pathogens and their interactions with the soil environment. In: Dixon GR, Tilston EL (eds) Chapter 6: soil microbiology and sustainable crop production. Springer, Dordrecht, pp 197–272 Donatelli M, Rizzoli AE (2008) A design for framework-independent model components of biophysical systems. In: Sànchez-Marrè M, Béjar J, Comas J, Rizzoli AE, Guariso G (eds) Proceedings of International Environmental Modelling and Software Society (iEMSs) 2008 International iEMSs Congress. Barcelona, Spain, pp 727–734 Donatelli M, Stöckle CO, Ceotto E, Rinaldi M (1997) Evaluation of CropSyst for cropping systems at two locations of northern and southern Italy. Eu J Agron 6:35–45. doi:10.1016/S1161-0301(96) 02029-1 Donatelli M, Cerrani D, Fanchini F, Fumagalli D, Rizzoli AE (2012a) Enhancing model reuse via component centered modelling frameworks: the vision and example realizations. In: Proceedings of International Environmental Modelling and Software Society (iEMSs), 2012 International IEMSs Congress, Managing resources of a limited planet (eds. Seppelt R, Voinov AA, Lange S, Bankamp D). Leipzig, Germany, pp 1185–1192 Donatelli M, Fumagalli D, Zucchini A, Duveiller G, Nelson RL, Baruth B (2012b) A EU27 database of daily weather data derived from climate change scenarios for use with crop simulation models. In: Seppelt R, Voinov AA, Lange S, Bankamp D (eds) Proceedings of International Environmental Modelling and Software Society (iEMSs), 2012 International IEMSs Congress, Managing resources of a limited planet. Leipzig, pp. 868–875 Donatelli M, Bregaglio S, Confalonieri R, De Mascellis R, Acutis M (2014) Comparing modelling solutions at sub-model level: a case on soil temperature simulation. Environ Modell Softw (in press ) Dosio A, Paruolo P (2011) Bias correction of the ENSEMBLES highresolution climate change projections for use by impact models: evaluation on the present climate. J Geophys Res 116, D16106. doi:10.1029/2011JD015934 El Araby ME, Kurle JE, Stetina RS (2003) First report of charcoal rot (Macrophomina phaseolina) on soybean in Minnesota. Plant Dis 87: 202. doi:10.1094/PDIS.2003.87.2.202C El-Hissy FT, Abdel-Kader MI (1980) Effect of five pesticides on the mycelial growth of some soil and pathogenic fungi. Z Allg Mikrobiol 20:257–263 European Commission (DG ENV) (2002) Integrated crop management system in the EU http://ec.europa.eu/environment/agriculture/pdf/ icm_finalreport.pdf Accessed 13 August 2013 Gilligan CA (1983) Modeling of soilborne pathogens. Annu Rev Plant Physiol Plant Mol Biol 21:45–64. doi:10.1146/annurev.py.21. 090183.000401 Glynne MD (1965) Crop sequence in relation to soil-borne pathogens. In: Baker KF, Snyder WC (eds) Ecology of soil-borne plant pathogens, prelude to biological control. University of California Press, Berkeley, pp 423–433 Gulya TJ, Krupinsky J, Draper M, Charlet LD (2002) First report of charcoal rot (Macrophomina phaseolina) on sunflower in North and South Dakota. Plant Dis 86:923–923. doi:10.1094/PDIS.2002.86.8. 923A Heffer LV, Johnson KB (2007) White mold. The plant health instructor. http://www.apsnet.org/edcenter/intropp/lessons/fungi/ascomycetes/ Pages/WhiteMold.aspx Accessed 13 August 2013 Hendrix FF, Campbell WA (1973) Pythiums as plant pathogens. Annu Rev Plant Physiol Plant Mol Biol 11:77–98 Hersh MH, Vilgalys R, Clark JS (2012) Evaluating the impacts of multiple generalist fungal pathogens on temperate tree seedling survival. Ecology 93:511–520. doi:10.1890/11-0598.1

Horton BJ (2012) Models for estimation of hourly soil temperature at 5cm depth and for degree-day accumulation from minimum and maximum soil temperature. Soil Res 50:447–454. doi:10.1071/SR12165 IPCC (2007). Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of working group 1 to the fourth assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge Jara J, Stöckle CO (1999) Simulation of water uptake in maize, using different levels of process detail. Agron J 91:256–265. doi:10.2134/ agronj1999.00021962009100020013x Jimenez Diaz RM, Blanco Lopez MA, Sackston WE (1983) Incidence and distribution of charcoal rot of sunflower caused by Macrophomina phaseolina in Spain. Plant Dis 67:1033–1036 Jones JW, Ritchie JT (1990) Crop growth models. In: Hoffman GJ, Howell TA, Solomon KH (eds) Management of farm irrigation systems, Chap. 4. St. Joseph, MI, USA, pp 63–89 Juroszek P, von Tiedemann A (2011) Potential strategies and future requirements for plant disease management under a changing climate. Plant Path 60:100–112. doi:10.1111/j.1365-3059.2010.02410. x Kim YK, Xiao CL, Rogers JD (2005) Influence of culture media and environmental factors on mycelial growth and pycnidial production of Sphaeropsis pyriputrescens. Mycologia 97:25–32. doi:10.3852/ mycologia.97.1.25 Kluitenberg GJ (2002) Heat capacity and specific heat. In: Dane JH, Topp GC (eds) Methods of soil analysis. Part 4. Physical methods. Madison, WI, USA, pp 1201–1208 Lakshmi V, Jackson TJ, Zehrfuhs D (2003) Soil moisture–temperature relationships: results from two field experiments. Hydrol Process 17: 3041–3057. doi:10.1002/hyp.1275 LaMondia J, Elmer WH, Mervosh TL, Cowles RS (2002) Integrated management of strawberry pests by rotation and intercropping. Crop Prot 21:837–846. doi:10.1016/S0261-2194(02)00050-9 Loo JA (2009) Ecological impacts of non-native invertebrates and fungi on terrestrial ecosystems. Biol Invasions 11:81–96. doi:10.1007/ s10530-008-9321-3 Manici LM, Caputo F, Cerato C (1995) Temperature response of isolates of Macrophomina phaseolina from different climatic regions of sunflower production in Italy. Plant Dis 79:834–838 Marín S, Sanchis V, Magan N (1995) Water activity, temperature, and pH effects on growth of Fusarium moniliforme and Fusarium proliferatum isolates from maize. Can J Microbiol 41:1063–1070 Mathre DE (1997) Compendium of Barley Diseases. In: Mathre DE (ed) 2nd ed. American Phytopathological Society, St.Paul, MN, p 120 Mehta YR (1998) Constraints on the integrated management of spot blotch of wheat. In: Duveiller E, Dubin HJ, Reeves J, McNab A (eds) Helminthosporium blights of wheat: spot blotch and tan spot, CIMMYT. Mexico, pp. 18–27 Micale F, Genovese G (2004), Methodology of the MARS crop yield forecasting system. Meteorological data collection, processing and analysis. Publications Office: European Communities, Italy Millar CS, Colhoun J (1969) Fusarium diseases in cereals: VI. Epidemiology of Fusarium nivale on wheat. Trans Br Mycol Soc 52:195–204 Millennium Ecosystem Assessment (MEA) (2005) Ecosystems and human well-being: synthesis. Island Press, Washington Neitsch SL, Arnold JG, Kiniry JR, Srinivasan R, Williams JR (2002) Soil and water assessment tool. User’s manual. Grassland, Soil and Water Research Laboratory, Agricultural Research Service, Temple Palmero D, de Cara M, Iglesias C, Tello JC (2009) The interactive effects of temperature and osmotic potential on the growth of aquatic isolates of Fusarium culmorum. Geomicrobiol J 26:321–325. doi: 10.1080/01490450902748641 Parton WJ (1984) Predicting soil temperatures in a shortgrass steppe. Soil Sci 138:93–101

Int J Biometeorol Parton WJ, Logan JA (1981) A model for diurnal variation in soil and air temperature. Agric Meteorol 23:205–216 Parton WJ, Hartman MD, Ojima DS, Schimel DS (1998) DAYCENT and its land surface submodel: description and testing. Global Planet Change 19:35–48. doi:10.1016/S0921-8181(98)00040-X Pettitt TR, Parry DW, Polley RW (1996) Effect of temperature on the incidence of nodal foot rot symptoms in winter wheat crops in England and Wales caused by Fusarium culmorum and Microdochium nivale. Agr Forest Meteorol 79:233–242. doi:10. 1016/0168-1923(95)02281-3 Porter JR, Semenov MA (2005) Crop responses to climatic variation. Philos T Roy Soc B 360:2021–2035. doi:10.1098/rstb.2005. 1752303 Rao VUM, Rao AVMS, Rao GGSN, Satyanarayana T, Manikandan N, Venkateshwarlu B (2011) Impact of climate change on crop water requirements and adaptation strategies. Chapter 24 in. Challenges and Opportunities in Agrometeorology. (Eds. Attri SD, Rathore LS, Sivakumar MVK, Dash SK) pp 311–319 Redman RS, Dunigan DD, Rodriguez RJ (2001) Fungal symbiosis: from mutualism to parasitism, who controls the outcome, host or invader? New Phytol 151:705–716. doi:10.1046/j.0028-646x.2001.00210.x Ritchie JT (1998) Soil water balance and plant water stress. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding Options for Agricultural Production. Kluwer Academic Publishers, Dordrecht, pp 41–54 Schwartz HF (2012) Root rots of dry beans. Fact sheet no. 2.938. Crop series: diseases. Colorado State University Cooperative Extension Service. http://www.ext.colostate.edu/pubs/crops/02938.pdf Accessed 13 August 2013 Smiley RW, Patterson LM (1996) Pathogenic fungi associated with Fusarium foot rot of winter wheat in the semiarid Pacific Northwest USA. Plant Dis 80:944–949

Stöckle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping systems simulation model. Eu J Agron 18:289–307. doi:10.1016/S11610301(02)00109-0 Sturz AV, Bernier CC (1989) Influence of crop rotations on winter wheat growth and yield in relation to the dynamics of pathogenic crown and root rot fungal complexes. Can J Plant Path 11:114–121 Tao F, Yokozawa M, Xu Y, Hayashi Y, Zhang Z (2006) Climate changes and trends in phenology and yields of field crops in China, 1981– 2000. Agric For Meteorol 138:82–92. doi:10.1016/j.agrformet. 2006.03.014 Vogt WG, Bedo D (2001) A preliminary weather-driven model for estimating the seasonal phenology and abundance of Lucilia cuprina. In: FLICS Conference (eds. Tasmanian Institute of Agricultural Research, University of Tasmania) pp. 62–64. Launceston, Tas Walthall CL, Hatfield J, Backlund P et al (2012) Climate change and agriculture in the United States: effects and adaptation. USDA Technical Bulletin 1935, Washington Wrather JA, Chambers AY, Fox JA, Moore WF, Sciumbato GL (1995) Soybean disease loss estimates for the southern United States, 1974 to 1994. Plant Dis 79:1076–1079 Yan W, Hunt LA (1999) An equation for modelling the temperature response of plants using only the cardinal temperatures. Ann Bot 84:607–614. doi:10.1006/anbo.1999.0955 Yang XB, Navi SS (2005) First report of charcoal rot epidemics caused by Macrophomina phaseolina in soybean in Iowa. Plant Dis 89:526– 526. doi:10.1094/PD-89-0526B Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14:415–421 Zveibil A, Freeman S (2005) First report of crown and root rot in strawberry caused by Macrophomina phaseolina in Israel. Plant Dis 89:1014–1014. doi:10.1094/PD-89-1014C