Pests and diseases are the major threats to world food security. More than 10% of yield loss is estimated to be caused by pests and disease every year, while in some regions the loss may higher than 30%. Wheat aphids and rust are important pests and diseases of wheat worldwide, causing significant crop losses in about 76 countries. Currently, monitoring and forecasting technologies mainly rely on human field surveys, but this information can only support mid- and long-term forecasting decisions. In aphid and rust management, there is a need for prediction of the occurrence area together with the damage levels. This requires habitat monitoring and early risk forecasting of wheat pest and disease in main wheat production regions global. Production of maps of the severity of aphid and rust would facilitate timely pest and disease management, thereby reducing yield losses and the use of chemical pesticides.
This Community Activity aims to bring together cutting-edge research to provide global pest and disease monitoring and forecast information. It will do this by integrating information from multiple sources, including satellite-based, meteorological, biological and plant protection datasets. In particular, the Community Activity aims to improve the use of Earth observation data for forecasting pest and disease through development of new algorithms and the fusion of new and existing data products using multi-source EO data to produce full cover, dynamic land surface information. The project will consider the capability of high spatial and temporal land surface information provided by moderate- to high- resolution satellite data (e.g. GF series, ZY series, HJ series in China, and Sentinel series in ESA, MODIS and Landsat in NASA) in wheat aphid and rust monitoring and mapping at the global scale. Approaches for better estimation of surface temperature statistics, diurnal surface temperature patterns, leaf area index (LAI) and vegetation dynamics will also be investigated. In addition, we will validate and prove the relevance of these data products to existing pest and disease development models and to forecast the potential distribution and damage levels of pests and disease. To ensure the project outcomes will have the greatest impact, the project will investigate best practices for dissemination of these information products.
WP1: Global wheat growth and pest & disease habitat monitoring
This work package will focus on the collection of the wheat growth information and habitat conditions with full consideration of sensing and pathological mechanism. The wheat growth conditions and soil moisture and temperature will be derived at the high spatial and temporal resolution satellite data from Sentinel-2, Landsat OLI, GF-6 et al. Data from the COSMOS sensors, and ground meteorological measurements will be used to improve the EO retrieval models, and the moderate to high resolution satellite, such as MODIS, would be used to calibrate and validate the retrieved EO products. Assimilating the land use investigation, meteorological data, field investigation, and epidemic mechanism of crop pests and diseases with the EO data, the remote estimates of land surface parameters and crop growth state for wheat aphid and rust habitat condition mapping would be produced to identify host habitats of wheat aphid and rust hotspots. It has been shown that differences in spectral and landscape patterns may be used to identify the habitats of aphid and rust.
WP2: Wheat pest and disease risk forecasting and warning
The aim of this work package is to integrate information from different sources (RS, biological indicators and meteorological data) to forecasting and mapping risks of wheat aphid and rust worldwide. The specific procedures are listed below:
1. The retrieved parameters in WP1 will be inputted into wheat aphid and rust habitat monitoring models that specific to different areas worldwide, and to assess the suitability of the habitat area for pest and disease overwintering and spring infection.
2. Develop a novel methodology and technology to integrate multi-source and multi-temporal EO observations, environmental parameters, biological models to characterize the evolution and risk probability of aphid and rust in wheat.
3. Establish a risk index for the early spring prediction using the locally recorded weather data at daily intervals.
4. Output a map describing the relative risks of wheat aphid and rust in the typical wheat planting countries and areas, based on the proposed model, RS data, and meteorological data for the typical phenological stage of winter wheat.
WP3: Application and dissemination
The aims of this work package are to integrate the outputs of WP1 and WP2 and to study and improve the two-way flow of information in prediction/advisory services to end users. These end users will likely include farmers, extension workers, and suppliers. RADI will work closely with CABI, the main information customer for the project outputs and the body with responsibility for public messaging. These will focus on identifying decision-making needs and enabling timely delivery of broadcast messages about pest and disease predictions along with suggested actions. Worldwide users will be able to benefit from these services through a web interface, wechat, e-mail, and/or interactive voice response messaging.
The Chinese and United Kingdom partners have gained rich experience in monitoring and forecasting of crop pests and diseases in China and worldwide over the past three years. Several cutting-edge technologies have been developed and have been widely used in exploring sensing and pathological mechanisms of wheat pests/disease, with the capability of water and nutrient stress differentiation.
Huang Wenjiang (China/CAS/RADI) email@example.com
Members: China, United Kingdom.
Leadership & Contributors (this list is being populated)