Crop mapping and zoning for strengthening sustainable agricultural practices and policy support through Earth observation applications in Pakistan

About the workshop

Capitalizing on recent advancements in artificial intelligence, Earth Observation big data, and cloud-computing platforms, the Pakistan Agricultural Research Council (PARC) and the International Centre for Integrated Mountain Development (ICIMOD) are working together to develop innovative data-science solutions that can support agricultural planning and food security assessments under ICIMOD’s SERVIR-HKH Initiative, a joint partnership between USAID and NASA. This collaboration also contributes to the scientific knowledge base on sustainable agricultural practices and policy formulation to ensure long-term food security in Pakistan.

Objectives

The overall objective of the consultation workshop is to engage key stakeholders and share the latest tools and innovations in EO, and identify the needs, opportunities, and complementarities to promote the use of EO tools and technologies in agricultural planning and policy formulation. The specific objectives include the following:

• Share knowledge on recent innovations in EO tools and technologies and their effectiveness in the agricultural planning and policy formulation
• Explore potential opportunities in the use of EO for key institutional functions in Pakistan’s agriculture sector
• Review the current data inventory to update agro-ecological zones of Pakistan and identify the potential data contributions from relevant agencies
• Deliberate on field data collection approaches for resource pooling and data-sharing mechanisms

Expected participants

Participants from national/provincial agencies and non-governmental organizations working in the areas of agriculture, climate change, food security, water, and irrigation are expected to  attend the workshop. 

Background

Pakistan’s agriculture sector plays a central role in the country’s economy as it contributes to about 19% of the gross domestic product and absorbs 42% of the labour force. Pakistan is presently self-sufficient in the production of major staple crops and is ranked 8^th in wheat production, 10^th in rice production, 5^th in sugarcane production, and 4^th in milk production. Despite these strong production figures, only 63.1% of the country’s households are food secure. In recent years, rapid economic development and urbanization have added more complexities to the food security challenge as these activities compete with the agriculture sector for finite land resources. As Pakistan’s agriculture depends on snow and glacial melt water resources for irrigation, the impacts of climate change are manifold. The projected changes in the future availability of meltwater and groundwater depletion may severely limit the agriculture sector’s ability to meet the needs of an ever-increasing population. A clear understanding of cropping patterns and historic changes along with the socioeconomic and ecological impacts of agricultural land use change in the region is essential to prepare well-informed policies.

Monitoring and estimating crop acreage and yield at a national scale is required to determine the national or regional food demand and supply balance, and to gauge food security. Whether during times of global food shortages or during periods of surplus, monitoring and estimating crop acreage requires long-term efforts. Along with food shortages resulting from an increased frequency and magnitude of extreme events, unsustainable agriculture practices like over-extraction of water, crop intensification, exhaustive use of chemical fertilizers, and virtual water trade damage ecosystem services and impair the long-term capacity to sustain high agricultural outputs.

With the recent advancements in EO technologies including freely available high-resolution satellite data, availability of machine-learning algorithms, and easy access to cloud-computing platforms like the Google Earth Engine, establishing systematic crop monitoring systems has now become feasible at lower costs and with greater efficiency. Such systems can greatly help perform in-season crop management and aid production-related decisions. In addition, long-term historic satellite data and land use modelling can be effectively utilized to quantify changes in croplands, review their current environmental suitability, and assess future implications to inform long-term decisions on agricultural land use policy and sustainability.