Plant Disease Forecasting and Weather Correlation
Since the outbreak of Covid-19 at the start of 2020, the world has not been the same. The need for a warning signal before a global pandemic cannot be stressed more highly. The same is the case in the plant world. Crop diseases have the potential to cause devastating epidemics that threaten the world's food supply and can cause huge economic losses. The Irish Potato Famine, also known as the Great Hunger, has been one of the greatest catastrophes in human history. Caused by a fungus called Phytophthora infestans which leads to late blight in potatoes resulting in the death of over a million people.
Plant Disease Forecasting
Just as the weather forecast provides plausible prognosis about weather conditions for upcoming days, plant disease forecasting is used to predict the occurrence or change in the severity of plant diseases. These are used by growers to make timely decisions about disease treatments to avoid losses. A crop disease forecasting system can not only be leveraged to build resilience to plant disease outbreaks but also enable better program assessment and adoption to pest management. Plant diseases are caused by the vast circle of diversified pathogens which continuously tend to undergo mutations, generating new strains for their survival. Forecasting systems primarily embed three elements - host, pathogen, and the environment also referred to as ‘the disease triangle’. A plant (the host) must be susceptible (in a vulnerable state); the parasite (the pathogen) that causes the disease must be in an infective stage, and the environmental conditions must be favourable for the disease development. The synchronous interaction between host, pathogen and the environment completes the triangle and guarantees disease development.
How Weather Affects Crop Diseases?
Effects of weather conditions on plant diseases are complex, influencing not only the events of the disease cycle but also the resistance of the plant (its ability to throw off or survive attacks).
Apple scab, caused by the fungus Veniuria inaequalis, attacks apples and ornamental crabapples. The infected fruits and leaves form scabby spots which may lead to secondary infections and premature dropping. Disease development is favoured by wet, cool weather that generally occurs in spring and early summer. During damp or rainy periods, newly opening apple leaves are extremely susceptible to infection. The longer the leaves remain wet, the more severe the infection will be. Apple scab spreads rapidly between 12 - 23 degrees celsius but the disease is obsolete in hot and dry regions.
In India, chickpea production in the last decade has changed drastically due to climate change. Caused by the fungus, Rhizoctonia bataticola, which is influenced by high temperatures (>33OC) and low soil moisture content, can exacerbate the disease to epidemic proportions due to further expected increase in average temperature and inconsistent rainfall patterns.
Climate change has surged the already calamitous situation of crop diseases. It influences the occurrence, prevalence, and severity of plant diseases. Temperature rise has major effects on crop yield as it governs the rate of reproduction for many pathogens, alters the epidemiology of plant diseases and can directly affect the spread of infectious diseases and their survival between seasons. Occurrence of bacterial diseases, for instance, such as Ralstonia solanacearum (causes Ralstonia wilt in over 40 plant families), Acidovorax avenae (causes infection which can result in the complete crop loss of several commercial species such as watermelons, cantaloupes, pumpkins) and Burkholderia glumea (causes blight in rice) could proliferate in areas where temperature-dependent diseases have not been previously observed.
Disease forecasting methods for some economically significant plants already exist like Potato late blight (Phytophthora infestans), Tobacco blue mould (Peronospora tabacina), Wheat brown (Leaf) rust (Puccinia triticina), among others. With a drastically changing climate, however, there exists a wide gap and to develop advanced forecasting models around the globe.
How New Disease Models Can be Developed?
Environmental factors strongly contribute to the occurrence and growth of plant pathogens. The Linear Regression Model is used to analyse the infliction of external field conditions on the crop. Regression analysis is a statistical process for estimating the relationship between a dependent variable (in this case, the disease) and one or more independent variables (in this case, the environmental factors - the amount of rainfall, temperature, humidity). At first, the variety of diseases that occur in a crop at its particular growth stage are identified. A sample of environmental factors, most crucial for the pathogen to cross the infection threshold, are considered for some time (1-4 weeks, depending upon the crop) and the severity of the disease is calculated.
For multiple linear regression, where several independent variables are present (as in this case), the correlation of environmental factors on the plant disease development can be calculated as:
where is the predicted or expected value of the dependent variable, X1 through Xp are p distinct independent or predictor variables, b0 is the value of Y when all of the independent variables (X1 through Xp) are equal to zero, and b1 through bp are the estimated regression coefficients.
Disease prediction not only benefits the farmers in reducing their losses but also gives humanity a strong chance to control the current food crisis.
Why is Weather Monitoring and Disease Prediction Model Required?
Pathogens require certain environmental conditions to thrive in. For example, Blister blight in tea can be kept in control with 4-5 hour of sunshine. However, a lesser number of sunshine hours will lead to an outbreak of the disease. Similarly, a temperature range of 19-23 Degree celsius for 14 continuous days will enhance the possibility of egg hatching of scarlet mite on leaves. Information about environmental conditions within the crop (the 'microclimate') has a more direct impact on disease than the large-scale weather forecasts. Access to microclimate local weather monitoring, discovers the possibility of disease by using statistical disease prediction models. A variety of devices have been developed for monitoring microclimatic factors such as duration of leaf wetness, temperature, soil moisture content that are important in plant pathology.
As an example, for tea blister prediction following regression equation can be used:
• Y = a+b1xr+b2x2
• Where Y = disease incidence,
• X1 = duration of leaf wetness,
• X2 = number of spores per litre of air
• a, b' and b2 are constants.
Yuktix GidaBits Weather Station and Nodes monitor real-time data like temperature, humidity, soil moisture content among others and record even the smallest changes in the farmland. These are then fed to the GidaBits cloud where they are processed and disease advisory is published as an actionable output.
Yuktix’s Weather Station Tool
Mahapatra, Sunita & Saha, Poly & Das, Srikanta. (2018). Plant disease forecasting in the era of climate change : Trends and applications.
V. Sellam and E. Poovammal. (2016). Prediction of Crop Yield using Regression Analysis. 10.17485/ijst/2016/v9i38/91714
Micheal Warren Shaw. (2009). Preparing for changes in plant disease due to climate change. 10.17221/2831-PPS