In order to adapt to the impacts of climate change, most countries in the region have developed National Adaptation Plans and Strategies and are now gearing up to implement them. ‘UAE-Belem work programme’ convened by UNFCCC is working on refining and developing indicators for measuring progress towards the Global Goal on adaptation in eight domains – water, food, health, ecosystems, infrastructure and human settlements, poverty and livelihoods, cultural heritage, and policy cycle.
Remote sensing involves gathering information from a distance, often using the power of satellites. Machine learning is a branch of artificial intelligence whereby computers use data and algorithms in order to learn for themselves, without being specifically programmed. The SERVIR Hindu Kush Himalaya (SERVIR-HKH) Initiative of the International Centre for Integrated Mountain Development (ICIMOD) and Nepal’s Ministry of Agriculture and Livestock Department (MoALD) have been using remote sensing (RS) and machine learning (ML) techniques to adopt new technologies in food security assessment since 2019.
In 2020–2021, MoALD used these technologies to generate in-season rice maps for 21 districts in the Terai, the lowland regions of southern Nepal. The team used RS imagery of croplands, and machine learning algorithms such as the ‘Random Forest classification technique’ to identify rice areas. These maps were then employed to ascertain the estimates of rice production which are further used to determine the Minimum Support Price (MSP) of rice each year. The MSP safeguards farmers by ensuring they receive a fair price, while also keeping rice affordable for consumers.
ICIMOD experts provided training to MoALD officials on using the Geographic Information System (GIS) services and RS data in crop mapping. ICIMOD conducted a series of knowledge-sharing workshops on EO-supported in-season crop area estimation in Nepal. These training sessions helped to enhance the technological knowledge of the MoALD agriculture professionals – they are now able to proficiently utilise GIT and EO in crop monitoring and damage assessment.
The training sessions also involved imparting technical skills to MoALD professionals, equipping them to effectively collect field data and utilise mobile applications such as GeoFairy, which is used to collect precise field data, and Google Earth Engine, which can be used for rice area mapping. Such training sessions, with their comprehensive insights into advanced techniques and tools, have empowered the agriculture ministry professionals to make informed decisions regarding crop management, monitoring, and damage assessment. The impact of these training sessions will help develop technical expertise working in this field within MoALD, leading to reliable and improved agricultural statistics, optimising resource utilisation, and ultimately strengthening food security measures in the areas served by MoALD.
The introduction of high-quality crop mapping has also bridged information gaps among federal and subnational institutions, and has now become a dependable resource for consistent crop status assessment and the communication of consistent agricultural information across the country. Moreover, despite being relatively new, this technology is already supporting community- and national-level agriculture management by providing scientific and reliable data.
This collaborative exercise between ICIMOD and MoALD has significantly boosted the Ministry’s confidence in employing sophisticated data-driven technologies. Looking to the long term, by using GIT and EO in crop monitoring, the overall resilience and efficiency of Nepal’s agricultural system stand to be well served. This infusion of state-of-the-art technology is a significant step towards the creation of a more informed and productive agricultural landscape, where food insecurity will no longer be an issue across the country and its communities.
ICIMOD stands as a robust pillar of support in our pursuit of reliable and precise agricultural data. This mutual relationship fosters a two-way commitment wherein MoALD and ICIMOD are collaborating with each other to enhance the accuracy of our agricultural data through satellite technology. With ICIMOD’s support, we are enriching our data generation systems and laying a strong foundation for policy tools and planning in the crucial area of agricultural monitoring.
-Dr. Govinda Prasad Sharma, Secretary, MoALD
Read here the related blog on ‘Satellite imagery in rice crop mapping: Sowing the seeds for stronger food security’