Whilst the spread of plant disease depends strongly on biological factors controlling transmission, epidemics clearly also have a human dimension. Disease control depends on decisions made by individual growers, who are in turn influenced by a broad range of factors. Despite this, human behaviour has rarely been included in plant epidemic models. This thesis focusses on addressing this oversight by developing combined epidemic and economic models of disease spread. We use simple continuous-time models of disease spread which we couple with behavioural models which set the management programme of the growers for the next growing season. Our models are rooted in game theory, with growers making strategic decisions based on the expected profitability of different control strategies.

In the first instance, we compare different versions of this behavioural model, which differ in terms of the information used by the growers to assess profitability. We investigate these models in the context of Cassava Brown Streak Disease (CBSD) and its management via the use of clean seed systems (CSS). We find that both the information used by growers to assess profitability and the perception of economic and epidemiological parameters influence long-term participation in the CSS. Over-estimation of infection risk leads to lower participation in the CSS, as growers perceive that paying for the CSS will be futile. Additionally, though the CSS can reduce the burden of disease, and allow a scenario in which all growers control, disease is not eliminated from the system.

For the remainder of this thesis, we use one behavioural model to investigate the deployment of crop that is either resistant or tolerant to Tomato Yellow Leaf Curl Virus (TYLCV). We find that when growers used resistant crop, higher yields were achieved by both controllers and non-controllers, though widespread use of resistant crop was not achieved. The use of tolerant crop reduced the yields for non-controllers, but generally benefited its users, inducing a positive feedback loop that resulted in a high proportion of growers using tolerant crop.

By extending this TYLCV model to allow a three-way choice of tolerant, resistant, and unimproved crop, we see again how growers prefer tolerant crop. However, these responses can be manipulated by changing the cost of each crop type through subsidisation schemes. To do this, we consider the efforts of a ``social planner" who moderates the price of crops. We find that subsidising tolerant crop costs the social planner more in subsidies, as its use encourages selfishness and widespread adoption. Subsidising resistant crop, however, increased the use of resistant crop, again enabling higher yields across the community of growers.

Many of our results obtained were robust to spatial and stochastic effects. Some differences arose when growers narrowed their information sources to only consider those growers whose fields are in close proximity to their own, as this allowed assessments of profitability to be based on local disease pressures.

In this thesis, we show how simple models of grower behaviour can be incorporated into both deterministic and spatial-stochastic models of disease spread. Understanding the influence of economic and epidemiological factors, as well as the feedback loops induced by different control mechanisms, on these behaviours can help to promote better outcomes for growers.