Regional Monitoring of Fall Armyworm (FAW) Using Early Warning Systems

Abstract

The second United Nations Sustainable Development Goal (SDG2), zero hunger, aims to improve the productivity, food security, nutrition, and sustainability of small-scale farmers. The fall armyworm (FAW, Spodoptera frugiperda) has been devasting to smallholder farmer food security since it spread to sub-Saharan Africa in 2016, who have suffered massive crop losses, particularly maize, an important staple for basic sustenance. Since the FAW mainly devours green leaf biomass during the maize vegetative growth stage, the implementation of remote sensing technologies offers opportunities for monitoring the FAW. Here, we developed and tested a Sentinel 2 a+b satellite-based monitoring algorithm based on optimized first-derivative NDVI time series analysis using Google Earth Engine. For validation, we first employed the FAO Fall Armyworm Monitoring and Early Warning System (FAMEWS) mobile app data from Kenya, and then subsequently conducted field validation campaigns in Zimbabwe, Kenya, and Tanzania. Additionally, we directly observed loss of green biomass during maize vegetative growth stages caused by the FAW, confirming the observed signals of loss of the leaf area index (LAI) and the total green biomass (via the NDVI). Preliminary analyses suggested that satellite monitoring of small-scale farmer fields at the regional level may be possible with an NDVI first-derivative time series anomaly analysis using ESA Sentinel 2 a+b (R2 = 0.81). Commercial nanosatellite constellations, such as PlanetScope, were also explored, which may offer benefits from greater spatial resolution and return interval frequency. Due to other confounding factors, such as clouds, intercropping, weeds, abiotic stresses, or even other biotic pests (e.g., locusts), validation results were mixed. Still, maize biomass anomaly detection for monitoring the FAW using satellite data could help confirm the presence of the FAW with the help of expanded field-based monitoring through the FAO FAMEWS app.

Rapid Assessment of Insect Pest Outbreak Using Drones: A Case Study with Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae) in Soybean Fields

“…As a result, obtaining suitable images for calculating NDVI values during a short pest outbreak period can be challenging. Our study is the first to detect the outbreak of S. exigua using NDVI, although a few previous studies have successfully demonstrated the potential of satellite-based NDVI in detecting outbreaks of the fall armyworm S. frugiperda in cornfields [ 40 , 41 ].…”

Career Development Activities for African Women, Students and Young Professionals in Geoscience and Remote Sensing

“…The 2 nd webinar was on September 19th, 2023, convened a discussion around cutting-edge remote sensing technologies being deployed to combat food insecurity [5]. Dr. Maria Luisa Buchaillot of the Fall Armyworm Monitoring and Early Warning System (FAMEWS) project [6] shared insights from FAMEWS' recent research leveraging satellite imagery to detect fall armyworm infestations and protect vulnerable smallholder farmers in Sub-Saharan Africa. Dr. Maria emphasized that the FAW has devastated maize crops across Africa since 2016, critically impacting food supply stability.…”

Sentinel-2, Herramienta en La Seguridad Agroalimentaria Y Agricultura De Precisión

“…Tal estudio utilizo el NDVI en calculadora ráster en QGis, sin embargo, para analizar los datos mediante regresión lineal utilizaron el nivel de infestación del gusano cogollero del maíz (GCM) como variable predictora de la primera derivada del NDVI (dNDVI). Buchaillot et al (2022), obtuvieron un R 2 : 0.8102 y la relación fue negativa, a medida que aumenta el GCM disminuyo el dNDVI en el cultivo de maíz, ello se debe a la alimentación del insecto, disminuye la masa de la planta y se refleja en el NDVI; los mismos autores concluyen que el homologar tal metodología en otros ambientes resulta de interés para la SA.…”

“…Esto agrega presión sobre los recursos naturales, el suelo sufre de degradación y aumenta la incertidumbre en torno la seguridad agroalimentaria (SA), a su vez incrementa la contaminación atmosférica y de cuerpos de agua. Es fundamental aplicar nuevas tecnologías que monitoree y haga eficiente el manejo de los cultivos para lograr los objetivos de desarrollo sustentable (ODS) (Buchaillot et al, 2022) y una tecnología que resalta es la agricultura de precisión (AP), que conlleva la responsabilidad de mejorar las prácticas convencionales de los agricultores, utilizando herramientas como análisis de macrodatos que implica mayor volumen y complejidad de datos que requieren de métodos de análisis no tradicionales, inteligencia artificial y análisis geoespacial (Marino, 2023).…”