Display name Geoffrey Anyanda First name Geoffrey Last name Anyanda email@example.com Role Researcher Country Kenya Organisation CIMMYT-Kenya Area of Research Socio-economics and Impact Assessment Describe your research
Direct estimation of maize yield loss caused by fall armyworm using six CIMMYT genotypes in artificial and on natural infestation in major maize growing agro ecologies in Kenya. In addition, i conducted a trial on the life table of the fall armyworm in controlled conditions using the same genotypes as natural diet for the larvae as well as for the fecundity studies.
ORCID iD Google Scholar Link Member since July 15, 2020 Topics posted 12 Replies 16
Can socio-economic and agro-ecological data be used to estimate food insecurity levels across house-holds in SSA as a function of exposure to FAW invasion risk, and vulnerability and lack of coping strategies among the exposed populations ?
December 10, 2020
Hello Steve Thank for this question. Well a range of studies have estimated yield loss caused by FAW in Africa. Kumela et al. (2018) reported a yield loss of 47% in Kenya based on farmers perceptions. A recent study in Kenya reported 34% yield loss caused by FAW in the long rains of 2017 and 32% in both the short rains of 2017 and long rains of 2018 using community surveys (De Groote et al., 2020). Actual experimental yield loss are usually very infrequent due to pest occurrences and also establishing good control plots as you mentioned. However, CIMMYT-Kenya is currently working on the first Country-wide yield loss assessment caused by FAW. This results are expected to be out by the end of the year.
December 11, 2020
Can augmentative releases of T. remus and T. chilonis in Africa effectively control the damage caused by FAW as compared to the full control ? In latin Latin America T. remus in maize fields showed 90% parasitism.
December 8, 2020
The assessment of yield loss associated to FAW is crucial for sustainable management option in Nepal. Extension of knowledge on the identification of this pest is Key. Thank you for sharing this @ruchita-bhattarai .
December 7, 2020
Hello Pearson To my knowledge this is the first time, monitoring network comprising of radar, pheromone traps, machine learning and decision support apps have been established in SSA. This is impressive work, it provides a baseline for adoption and a more realistic development of digital monitoring in SSA.
December 1, 2020