Late Blight 

Developing late blight forecasting  and early warning system for potato and tomato in Nepal

Introduction and Background

Tomatoes and potatoes are the most important crops for food and nutritional security in Nepal. Both of these crops are devastatingly damaged annually by late blight whose causative agent is oomycetes Phytophthora infestans. The disease can cause 100% yield loss under conducive local climate. The use of resistance variety and the application of fungicides are two important strategies to manage the diseases. However, the rapid evolvability of oomycetes Phytophthora infestans makes its management tricky even with resistant varieties. Therefore, well timed application of fungicide is crucial for the management of the disease. 

 In the absence of early warning systems for late blight, farmers end up haphazardly using fungicides to control the disease. The haphazard use of fungicides exposes consumers to chronic health risks such as cancer, immunotoxicity, abnormality in neurological and physical development, and disruption of the endocrine system. Moreover, it adds financial burden to farmers which either gets passed on to consumers or causes farmers to bear the loss. Lastly, the haphazard use of pesticides negatively affects useful plants and insects, and pollutes land and water resources. 

Recently, an Indo-Bligh-Cast, a late blight forecasting model was developed in india. It has been successfully implemented across India to forecast the late blight, and advise growers for advance application of fungicides. The report indicates that scheduling fungicides as per Indo-Blight-Cast reduced 22.2 to 44.4% in fungicide use. Hence, to reduce the application of fungicides haphazardly in Nepal, Awasar Agritech, in collaboration with our partners, is developing blight cast for Nepal, i.e Nepal-Blight-Cast. 

Progress so far

We have developed a working prototype of BlightCastNepal (https://www.blightcastnepal.com/). It uses the same kind of computation as Indo-blight-Cast. Indo-Blight-Cast is one of the most successful late blight forecasting models developed in India. Currently we are testing and validating the Nepal-Blight-Cast using the historical data generated in Nepal. Our model’s results are on par with the Indo-Blast-Cast. Similarity of weather pattern in Nepal to weather pattern in India was the reason we decided to use a similar computation as Indo-Blast-Cast.  

Immediate Plan

Next, we are working towards integrating the model into both web-based and mobile-based applications where weather parameters will be automatically fed to provide the automatic warning system to alert farmers and other concerned stakeholders to apply fungicide of the right kind at the right time in the right amount. 

Similarly, to make Nepal-Blast-Cast an absolutely robust model, we are in the process of  conducting at least 60 experimental trials. For the trials we are using 2 tomato/potato varieties with varying levels of late blight resistance. Lastly, we are varying pesticide spray conditions, that is, spray and no spray, and geographical location across Nepal to collect high quality data.