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Agricultural Information Systems - Building Provincial Capacity for Crop Estimation and Forecasting in Pakistan

Project Description

Pakistan is a country of diverse agro-climatic regions. Agriculture plays a pivotal role as it contributes to more than 20% of the GDP and provides employment to 45% of the labor force. The major crop during the winter season (Rabi) is wheat, during the summer season (Kharif) cotton, rice, sugarcane and maize. The conventional system employed by the provincial government for collecting agricultural statistical data relies on labor- and time-intensive field data collection in a small percentage of villages. The data are acquired through manual surveys and enumeration techniques and data compilation and analysis is completed several months after harvest.

The multi-agency project funded by USDA, "Agricultural Information System - Building Provincial Capacity in Pakistan for Crop Estimation, Forecasting, and Reporting based on the integral use of Remotely Sensed Data; GCP/PAK/125/USA" focuses on enhancing and improving current systems for the integral use of remotely sensed data into existing data collection, analysis, and dissemination systems as well as the development of complementary systems to enable the integration of satellite remotely-sensed data and improved field estimates for area and yield forecasting and estimation as well as continuous crop status monitoring. More information on this project carried out in partnership with SUPARCO, Agri Punjab, USDA, and UMD can be found here.

Project activities include re-designing the Global Agricultural Monitoring System (GLAM) in close collaboration with the partners in Pakistan to meet the specific needs of the local analysts. UMD is also developing a cell-phone based data collection system Geo-ODK/MAGIS for more efficient field data collection and quick delivery of the collected field data to the CRS head office. In collaboration with the University of Agriculture Faisalabad (UAF) and Sindh Agriculture University, Tandojam (SAUT) the University of Maryland is supporting the CRSs in the interpretation of the imagery and vegetation index time series generated by GLAM. UMD and UAF are also working on developing annual early-season wheat crop indicator layers and on pre-harvest wheat yield forecasting. In a complementary project UN-FAO and SUPARCO are working with the CRSs in designing and implementing an area-frame based agricultural statistics system utilizing SPOT and Landsat imagery. An important outcome of the project are crop bulletins published monthly by the CRSs with active support of all participating organizations.