Summary of our research
Rice consumption in Sub-Saharan Africa is greatly increasing due to urbanization and changes in consumer preferences from traditional staples to rice. In most Sub-Saharan African countries, however, productivity of rice is still low and the increasing demand for rice is met by importing rice from the international market. In addition to water scarcity and poor mechanization, rice yellow mottle virus (RYMV) disease is regarded as a major constraint that causes the low productivity of rice in Sub-Saharan Africa.
The mechanism of RYMV transmission at the plant community level has been well established, with epidemiological models proposed. The long-distance dispersal model from its origin in East Africa to West Africa has been also well established phylogeographically. However, the conditions causing local disease outbreaks, the missing link between the plant community level and continental scale models, remain unclear.
A group of scientists led by Mie University professors (*1) investigated a RYMV outbreak in Tanzania. They established spatial autoregressive models (*2) based on three different disciplines; plant pathology (virus detection), crop science (field survey), and economics (farmer interviews, econometric analysis) and successfully identified farming practices that are strongly associated with the occurrence of disease clusters.
In agronomic studies, spatial autoregressive models have been successful, but limited to smaller-scale field experiments. This is the first study to upscale the spatial autoregressive model from the experimental field level to the farming community level, by obtaining variables through easy-to-implement techniques such as visual observation and farmer interview.
Previously, there has been no effective measures against rice yellow mottle virus disease. This study has demonstrated that the disease can be prevented by improving farming practices which can be implemented by rice farmers in Sub-Saharan Africa cost-effectively and sustainably. The outcome of this study is expected to contribute to the improvement of rice production and by extension food production in Sub-Saharan Africa.
*1: An international research team formed by Mie University (Professors SEKIYA Nobuhito and NAKAJIMA Toru of Graduate School of Bioresources), Japan International Cooperation Agency (JICA, Mr. Nobuaki Oizumi and Mr. TOMITAKA Motonori), Tokyo University of Agriculture (Professor NATSUAKI Keiko), Sokoine University of Agriculture (Mr. Naswiru Tibanyendela), and Kilimanjaro Agricultural Training Centre (Mr. Mchuno Alfred Peter).
*2: A multiple regression model that considers spatial contiguity, where the occurrence of rice yellow mottle virus disease (response variables) is regressed on farmers' farming practices (explanatory variables) and spatial contiguity (spatial autocorrelation ≈ adjacent rice fields are likely to be infected with rice yellow mottle virus ≈ adjacent rice field are likely to be a cluster of disease infection).
Professor, Graduate School of Bioresources
Current research field:
Sustainable Rice Production (Organic Rice Farming)
Associate Professor, Graduate School of Bioresources
Agricultural Economics, Econometrics
Current research field:
Research on consumers' food demand for creating strong Japanese agricultural structure using machine learning methods