Written by Renan Macedo at the Universidade Federal de Goiás, Brazil. This is the report from a BSPP Junior Fellowship. Click here to read more/apply for one yourself.
Models of disease spread and distribution are pivotal for avoiding outbreaks and to support management decisions. Root rots are a worldwide constraint for several crops causing yield losses until 100% in outbreaks, such as the common bean (Phaseolus vulgaris L.), an essential protein source for diverse social classes, especially in developing countries. Common beans are frequently subjected to yield losses originated by root damage caused in special by Fusarium solani species complex. This disease therefore represents a ubiquitous problem for of food security and is usually controlled by intense use of chemicals.
Specifically, the visit to Dr Stephen Parnell’s lab at the University of Salford allowed the development of a smartphone app from a risk model created in the EMBRAPA Rice & Beans, supervised by Murillo Lobo Junior and Universidade Federal de Goiás, Brazil. This model was based on experiments of dry root rot severity in common bean planted in soil infested with an average of 5000 chlamidospores /g soil, in four levels of soil available water (50-60%, 80-90%, 110-120% e 140-150%, as fixed effect) and across the natural temperature range (13°C-20°C to 30°C-38°C, as random effect), utilising chemical (carboxin + thiram) and biological (Trichoderma harzianum) as seed treatments (fixed effect). Each treatment was repeated in triplicate and each experiment was repeated nine times (one year) in a glasshouse and we estimated root dry weight, shoot dry weight, leaf area and root architecture. The model-validation will be performed with in-farm retrieved data by cross validation calculate the expected efficiency of biological control. To identify the relevant data and format to be used in the tool “Shiny R” (shiny.rstudio.com), we used models calibrated with spatial risk data from my project in Brazil, in which we evaluated disease severity under abiotic stress; to learn how to program GUIs in R and to develop an online tool for targeted biological control of disease.
This is a brief description about utilities of the web app:
1. “Input data and functionalities”: this app may be useful to manage the dry root rot because it combines different types of input, such as weather and climate variables, to advise about the best option of disease control. This app version shows four tabs to interact with user and a panel to input information about the presence or absence of irrigation in farm. If irrigation is present, the user can choose which level of soil available water (40-50%, 80-90%, 110-120% and 140-150%), and current month. Then, messages about control will be displayed. The control is indicated according to last 10 days of weather records, 10 days weather forecast, climate data (1961 – 1990) or weather stations in farm (whenever there is one). Information about products and cultivars are also available in this app.
2. “Management of Disease” tab: this tab shows a display with an interactive localisation map (Latitude, Longitude) to indicate regions more suitable to control according to records of 30 years from Climond database. Indeed, a weather forecast is displayed in an interactive figure showing average maximum and minimal temperature, and average relative humidity. When the user selects these options, advices are displayed in the first tab indicating the best control option (Biological, Chemical or both). The user therefore may choose the best option to control dry rot root. However, we highlight that the user will be aware that regions more suitable to control may shows adverse weather conditions and it can change the advice messages.
3. “Products” tab: this second tab shows information about several chemical and biological products that are available and have been already registered in Brazil to control dry root rot in common beans.
4. “Tolerance of Cultivars” tab: third tab shows a list of cultivars indicating the most tolerant or sensitive cultivars to Fusarium dry root rot. Indeed, there is a list about tolerance of cultivars to rots caused by Rhizoctonia solani, because the presence this pathogen in farm may indicate an increasing of severity of rots caused by F. solani.
5. “References” tab: fourth tab shows references utilised to indicate chemical and biological products, climate database, weather database and control indications.
The BSPP fellowship enabled me to begin to develop skills in modeling and programming as well as to initiate new collaborations within the UK, which is particularly important in my career as an early scientist. I am very grateful for the opportunity received from British Society for Plant Pathology. This fellowship was an important learning opportunity for me because it was possible to exchange experiences with Dr Parnell’s team. As a postgraduate student based in Brazil, I recognise the necessity of building links with other senior scientists to develop my career and to contribute for the increase of scientific knowledge in the world. The expertise of Dr Parnell’s team improved my skills in R and the knowledge of predictive modelling in epidemiology. Also, it was very important to improve my academic experience. Due to the expertise achieved, I got a job as a researcher in Agrosmart SA, a prominent company in smart agriculture at Latin American, in which I am developing models to predict outbreaks on plant diseases. Fantastic! Thank you!
Universidade Federal de Goiás, Brazil