Browsing by Author "Bryony Taylor"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Publication
    Forecasting the Population Development of within-Season Insect Crop Pests in Sub-Saharan Africa: The Pest Risk Information Service
    (Journal of Integrated Pest Management, 2024-01-22) Day Charlotte; Sean T Murphy; Jon Styles; Bryony Taylor; Tim Beale; William Holland; Frances William; Andy Shaw; Cambria Finegold; MaryLucy Oronje; Birgitta Oppong-Mensah; Noah Phiri; Alyssa Lowry; Elizabeth A Finch; Josephine Mahony; Suzy Wood; Léna Durocher-Granger; Duncan Chacha; Norbert Maczey; Pablo Gonzalez-Moreno; Sarah E Thomas; Joe Beeken; Jane Lewis; Gerado Lopez Saldana; Solomon Duah; Mary Bundi; Lusike Wasilwa; Ruth Amata; Ruth Musila; Daniel Mutisya; Christine K Gitonga; Patrick Kalama; Johnson O Nyasani; Matthews Matimelo; Henry Mgomba; Christopher Gaitu; Christopher Ocloo; Isaac Adjei-Mensah; Godfried Ohene-Mensah; Jerry Asalma Nboyine; Blessings Susuwele
    Smallholder farmers are the mainstay of the agricultural economies of sub-Saharan Africa (SSA), where they produce several crops, predominantly centered on maize. Smallholder productivity remains limited resulting from a range of confounding factors, but a primary cause is loss from pests and diseases, particularly insects. To improve productivity, recommendations for the mitigation of crop loss globally include early-warning and management systems for in-season indigenous pests. There are many early-warning systems in temperate regions; however, such systems are poorly established in Africa. This is in part due to the need for a combination of pest modeling, data handling and dissemination infrastructure, capacity, and resource provision. While each of these components is progressing in Africa, the means to successfully deploy such systems remain limited. To bridge this, the development of the Pest Risk Information Service (PRISE) began in 2017 for farmers in SSA. Implemented in Kenya, Ghana, Malawi, and Zambia, PRISE developed temperature-driven phenology models for major maize, bean, and tomato pests. Using downscaled and processed Earth Observation data to drive the models, PRISE partnered with African national agencies to communicate pre- and in-season pest alerts that forecast the time to act against key insect pests. Alerts were designed to be integrated into country-specific Good Agricultural Practice (GAP) recommendations to provide a complementary package to agricultural stakeholders. End line studies with farmers showed that those who received information about the target crops including PRISE pest forecasts, generally reported better outcomes in terms of reduced losses and increased incomes compared with farmers who did not.