Modelling crop diseases for food security

Al-Azri, Masoud (2017) Modelling crop diseases for food security. PhD thesis, University of Nottingham.

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Global crop production is affected by seasonal and climatic variations in temperature, rainfall patterns or intensity and the occurrence of abiotic and biotic stresses. Climate change can alter pest and pathogen populations as well as pathogen complexes that pose an enormous risk to crop yields and future food security. Crop simulation models have been validated as an important tool for the development of more resilient agricultural systems and improved decision making for growers. The Agricultural Production Systems Simulator (APSIM) is a software tool that enables sub-models to be incorporated for simulation of production in diverse agricultural systems. Modification of APSIM to incorporate epidemiological disease model for crop growth and yield under different disease intensities has few attempts in the UK or elsewhere. The overall aim of this project is to model disease impact on wheat for improved food security in two different agro-ecological zones.

The incidence of wheat diseases between 2009 and 2014 in two different agro-ecological zones, UK and Oman were compared. Most of the fields surveyed in Oman and UK were found to have at least one disease. Leaf spot was the most prevalent foliar disease found in Omani fields while Septoria was the most common foliar disease in the UK. Fusarium followed by eyespot and ear blight represents the most common diseases of stem and ears in UK winter wheat between 2009 and 2014. However, in Omani wheat Fusarium causing stem base and loose smut of ears were the most common. Eyespot was not found in Omani winter wheat and this may relate to the high temperature during winter in Oman. This study discussed the first work on the occurrence of fungal diseases and their pathogens in Oman and the influence of agronomy factors. Large numbers of pathogenic fungi causing symptoms were found to be prevalent in wheat fields in Oman. Isolation from six symptomatic wheat varieties resulted in 36 different fungal species. Alternaria alternata was the most frequently isolated pathogen followed by Bipolaris sorokiniana, Setosphaeria rostrata, and Fusarium equiseti. Results also showed some agronomic practices influenced disease incidence. Mechanical sowing method and time of urea application were found to influence leaf spot disease.

An investigation into the recovery of treatment cost for eyespot control through yield and the effect of fungicide treatment on risk showed that all fungicides apart from (epoxiconazole) Opus at 1 L ha-1 were found to be worth the costs, either under high disease pressure (inoculated sites) or naturally infected sites. For the risk averse manger fungicide treatment would be worth the cost as it would reduce the higher level of disease and consequently minimise associated yield losses.

In this work, disease models were built to predict the disease development and yield loss in relation to crop phenology using results from previous literature on conditions favouring sporulation, infection and disease development and severity. Analysis of 461 data sets showed that climatic conditions and agronomic factors significantly influenced disease development either positively or negatively in all models. The application of a range of fungicides at GS31/32 reduced disease significantly at GS39 in comparison to epoxiconazole alone. Disease severity at GS39 decreased yield only slightly by 2.2% whilst only (prothioconazole) Proline 275 increased yield significantly with almost 30% yield increase.

The performance of the APSIM wheat model to simulate phenology, leaf area index, biomass and grain yield of two winter wheat varieties (Okley and Cashel) was evaluated under UK conditions and the previously developed eyespot disease were linked with APSIM. Generally, APSIM poorly predicted the phenology, LAI, biomass and yield of winter wheat grown under UK conditions. The linked eyespot disease models with APSIM simulated an adequate level of disease predication at GS12/13 (9.6%), GS31/32 (1.3%) and GS39 (12%).

Overall, the link between eyespot epidemiological disease models and crop growth model has successfully provided the basis for further development of the model and enhance crop growth simulation. Moreover identification of main diseases threatening wheat production in Oman can help to plan for future research, to assess the economic importance and to contrast environment models for yield loss.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Ray, Rumiana
Ramsden, Stephen
Leibovici, Didier
Keywords: plant diseases, crop diseases, models, food security
Subjects: S Agriculture > SB Plant culture
Faculties/Schools: UK Campuses > Faculty of Science > School of Biosciences
Item ID: 44777
Depositing User: Al-Azri, Mr. Masoud
Date Deposited: 14 Dec 2017 04:40
Last Modified: 15 Dec 2017 03:03

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