*2.5.7. Prediction systems*

*2.5.2. Crop rotation*

46 Mycotoxins - Impact and Management Strategies

*2.5.3. Tillage practises*

*2.5.4. Managing plant stressors*

mycotoxin-producing interaction [95].

toxin accumulation in maize kernels [91].

*2.5.5. Chemical control*

The primary objective of cultural control of mycotoxigenic fungi is to minimise factors that result in plant stress. Inoculum build-up on plant residues can be reduced by crop rotation practices, such as the rotation of maize with non-host crops [75, 81, 82]. Crop rotation with legumes, brassicas and potato could also significantly reduce *F. graminearum* contamination levels [83].

Field preparation and cultivation practices play a central role in the management of *Fusarium* diseases and associated mycotoxins [84]. The burial of plant residues from a previous planting season by deep ploughing can reduce the primary inoculum that causes infections [85]. This is especially important when crops are affected by the same *Fusarium* species, such as *F. graminearum* on maize, wheat and sorghum grown in rotation [4]. While minimum tillage has significantly decreased stalk rot and increased grain yield of sorghum in South Africa [86], it has also increased inoculum build-up of mycotoxigenic fungi in maize cropping systems [84]. Alternate

tillage practices, however, have had little effect on the incidence of FER in maize [87, 88].

Limiting plant stress to increase plant vigour by adhering to optimum plant dates, preventing drought stress and the optimal use of fertilisers have reduced *Fusarium* infection in a number of grain crops [76, 89–91]. However, maize cultivated by means of organic agriculture does not accumulate less FUM than maize cultivated conventionally [92, 93]. Extended periods of heat and drought stress that lead to increased FUM levels could be managed with proper irrigation schedules [77, 94]. Managing plant stress conditions is also important as this is considered key in the symptomless endophytic relationship converting to a disease- and/or

Fungicides have been shown to significantly reduce FHB and DON contamination of wheat grain. Triazole fungicides such as metconazole and tebuconazole have been shown to control FHB and DON contamination in wheat [96]. However, fungicides are neither effective in reducing *F. verticillioides* infection/FUM accumulation, nor *A. flavus* infection/AF accumulation in maize [97]. This may be due to the husks that cover maize kernels. FUM were, however, reduced by 95% *in vitro* when four fungicides and a biocontrol bacterium (Serenade, *B. subtilis*) were evaluated for the control of *F. verticillioides* and *A. flavus* [98]. No registered fungicides are available for the control of either *F. verticillioides* or *A. flavus* in any African country [98]. The use of insecticides can prevent insect wounds that contribute to fungal infection and myco-

Reduced FHB severity and mycotoxin contamination of wheat under field conditions using tannic acid and the botanicals, Chinese galls and buckthorn, have been shown [100]. These researchers also reported disease and mycotoxin reduction efficacy close to that observed with a synthetic fungicide, thereby demonstrating the potential use of natural compounds An epidemic can be described as a 'change in disease intensity in a host population over time and space' [108]. Mathematical modelling of crop disease is a rapidly expanding discipline within plant pathology [109] with the first models developed by Van der Plank [110, 111]. In epidemiology, modelling aims to understand the main determinants of epidemic development in order to address disease management in a sustainable and efficient manner. It can, therefore, serve as an instrument to monitor and assess the risk of mycotoxin contamination in crops that would drive agronomic decisions during cultivation, in order to enhance management strategies [112].

Most research regarding disease forecasting of mycotoxigenic fungi has focussed on FHB of wheat. This disease is considered well suited for risk assessment modelling because of the severity of epidemics, compound losses resulting from mycotoxin contamination and relatively narrow time periods of pathogen sporulation, inoculum dispersal and host infection [113]. This can be seen from the online forecasting model FusaProg [114], which is a threshold-based tool to control *F. graminearum* with the optimised timing of fungicide applications and forecasts of DON content during flowering. DONCast is a prediction model from Canada that has been extensively validated and commercialised for wheat [112], while an adaption of this model has been proposed for maize. This model predicts the variation in mycotoxin levels associated with the year and agronomic effects from simple linear models using wheat samples from farmers. The DONCast model accounts for up to 80% of the variation in DON and is commercially employed for the past 10 years.

Field-based models to predict FUM B1 contamination in maize grain have been elusive, most probably due to the complexity of interactions between numerous abiotic and biotic disease factors [115]. The concentration and severity of FUM produced by *Fusarium* spp. varies with meteorological conditions, genotype and location [19]. In general, favourable conditions for *F. verticillioides* infection include high temperatures [56], drought stress [56, 116] and insect damage stress [56]. A mathematical simulation of the growth of *F. graminearum* and *F. verticillioides* in maize ears was developed; however, the model only simulates fungal growth and not mycotoxin accumulation [117]. A preliminary model developed in the Philippines and Argentina identified four weather periods near silking as critical to FUM accumulation at harvest [19]. This model accounted for 82% of the variability of total FUM across all locations in 2 years of study, but did not consider meteorological conditions during grain maturation when FUM are synthesised.

**Acknowledgements**

edged for funding.

**Author details**

Lindy J. Rose<sup>1</sup>

Altus Viljoen<sup>1</sup>

**References**

Francisco; 2006

2014/352831

**Conflict of interest**

The authors declare no conflict of interest.

\*, Sheila Okoth<sup>2</sup>

2 University of Nairobi, Nairobi, Kenya

\*Address all correspondence to: lindym@sun.ac.za 1 Stellenbosch University, Stellenbosch, South Africa

Medicine and Biology. 2002;**504**:257-269

2017;**773**:3. DOI: 10.17159/sajs.2017/20160121

3 Agricultural Research Council, Potchefstroom, South Africa

, Bradley C. Flett<sup>3</sup>

[1] Van Egmond HP. Worldwide regulations for mycotoxins. Advances in Experimental

[2] Barug D, Van Egmond H, Lopez-Garcia R, Van Osenbruggen T, Visconti A. In: Meeting the Mycotoxins Menace. The Netherlands: Wageningen Academic Publishers; 2003 [3] Fellinger A. Worldwide mycotoxin regulations and analytical challenges. In: World Grain Summit: Foods and Beverages; 17-20 September. Vol. 2006. California, USA: San

[4] Beukes I, Rose LJ, Shephard GS, Flett BC, Viljoen A. Mycotoxigenic *Fusarium* species associated with grain crops in South Africa—A review. South African Journal of Science.

[5] Warburton ML, Williams WP. Aflatoxin resistance in maize: What have we learned lately? Advances in Botany. 2014;**2014**:10. Article ID: 352831. https://doi.org/10.1155/

, Belinda Janse van Rensburg<sup>3</sup>

and

http://dx.doi.org/10.5772/intechopen.76808

49

The South African Maize Trust and the National Research Foundation (NRF) of South Africa (Thuthuka; South Africa—Kenya Research Partnership Programme Bilateral); the MAIZE Competitive Grants Initiative, International Maize and Wheat Improvement Centre (CIMMYT), and CGIAR, the National Commission for Science, Technology and Innovation (NACOSTI) of Kenya; the Agricultural Research Council of South Africa are all acknowl-

Preharvest Management Strategies and Their Impact on Mycotoxigenic Fungi and Associated Mycotoxins

A risk assessment model (FUMAgrain) developed for FUM contamination of maize grain in Italy gives an initial risk alert at the end of flowering based on meteorological conditions [118]. A second alert follows at kernel maturation following assessments of grain moisture, European corn borer damage and FUM synthesis risk. FUMAgrain could simulate FUM synthesis in maize accounting for 70% of the variation for calibration and 71% for validation. The importance of meteorological conditions at flowering and the growth of *F. verticillioides* and FUM synthesis during grain maturation was emphasised as the most important factors contributing to FUM contamination [118]. Another model consistently identified mean maximum temperature and minimum humidity as driving variables in the colonisation of maize kernels by fumonisin-producing *Fusarium* spp [99]. Furthermore, *Fusarium* colonisation of grain and fumonisins were related to prevailing weather conditions during early post-flowering and dough stage of grain development, respectively [99]. A prediction model using variables such as cultivar, climate, management practice, soil type, phenological stages of the host plant and pathogen variation would be advantages in identifying areas with potentially dangerous levels of fungal contamination and associated mycotoxin production, enabling them to implement mycotoxin management strategies.
