PROJECT PROPOSAL

1. APPLICATION OF REMOTE SENSING/GIS FOR AGRICULTURAL DATA CAPTURE AND MANAGEMENT FOR FOOD SECURITY ASSURANCE PROJECT PROPOSAL Statistics Research and Information Directorate (SRID) Ministry of Food and Agriculture (MoFA), Ghana

2. INTRODUCTION - AGRICULTURAL STATISTICS OF GHANA DEMOGRAPHY • Population =24.66 Million • Population growth rate = 2.5%/annum(2010 Census) GDP CONTRIBUTION • Agric. Product (GDP) growth rate = 6 % • Agriculture Growth Rate = 4% • Crops GDP contribution = 66%

3. AGRICULTURE STATISTICS-GHANA LAND • Total Land area = 23,853,900ha • Agricultural land Area=13,628,179ha (57.1%) • Cultivated Area= 7,458,000ha (54.7%) CROPS Staples: (Cassava, Cocoyam, Yam, Maize, Rice, Millet, Sorghum, Plantain) Vegetables : (Tomato, Pepper, Okro, Egg Plant, Onion, Asian Vegetables ) Fruits: (Pineapple, Citrus, Banana, Cashew, Pawpaw, Mangoes, Industrial Crops: (Cocoa, Oilpalm, Coconut, Coffee, Cotton, Kola, and Rubber)

4. MANDATE OF SRID/MoFA • ESTIMATE CROP AREAS, YIELD AND PRODUCTION OF 8 MAJOR FOOD & TREE CROPS • DETERMINE AVAILABLE WATER RESOURCE FOR PRODUCTION* • DETERMINES NET FOOD PRODUCTION, ESTIMATES FOOD DEMAND AND FOOD BALANCE SHEET • PROVIDES FOOD SECURITY EARLY WARNING SIGNALS

5. STRENGHT AND WEAKNESSES OF SRID/MOFA IN AGRICULTURE STATISTICS MANAGEMENT STRENGHT WEAKNESSES • OBSOLATE DATA COLLECTION METHODS The compass and tape method results in less area coverage for data collection) • LESS CAPACITY (STAFF AND EQUIPMENT) MoFA commits as much as 19% of its staff to just sample surveys • HIGH COST AND TIME CONSUMING SURVEYS. Cost of Annual Surveys= U$ 2million

6. WHY RS-GIS IN AGRICULTURE DATA MANAGEMENT - CAPABILITIES • Map the extent and distribution of individual crop types • Ability to capture, store, manage and update agricultural data • Monitor agricultural conditions and early assessment of production • Improved quality of decision making by government officials given the timely and up-to-date access to reliable information • Enhance Strategic planning

7. NATURE OF PROJECT • Development of RS/GIS-based PROTOCOLS and Spatial Database System for collection, Storage and Maintenance of Agricultural Statistics through Capacity Building of MoFA/SRID Staff • Design and implement a practical and operational approach for the collection & management of agricultural statistics in Ghana on annual basis using a methodology that combines geospatial technologies (remote sensing, GIS, GNSS) and targeted field data collection at LOCAL/DISTRICT LEVEL.

8. WHY THIS PROJECT - BENEFITS TO SRID/MOFA • REDUCED COST OF SAMPLE SURVEYS • REDUCE STAFF STRENGHT FOR SAMPLE SURVEYS • ENSURES ACURATE & RELIABLE DATA • EASY MANAGEMENT OF DATA - ASIS • IMPROVE POLICY • SOUND PLANNING • CAPACITY BUILDING IN RS/GIS

9. EXPECTED OUTPUTS & OTHER APPLICATIONS EXPECTED OUTPUTS OTHER APPLICATIONS • ESTABLISHED CUSTOMIZED WEB-BASED AGRICULTURAL SPATIAL INFORMATION SYSTEM (ASIS) (e.g. spatial and a-spatial information components) in a GIS-database form that enables storage, data search and access, cartographic overlay operations and the linking to models for crop production estimation). This ASIS can be viewed as the future spatial decision support system for MoFA’s decisions regarding agriculture and sustainable development. • ESTABLISHED PROTOCOL for standardized annual data collection and database updatingof agricultural statistics of major crops of Ghana based on the integrated use of remote sensing, GIS, GNSS and field data. • ESTABLISHED PROTOCOL for the integration of the GIS-based MoFA-ASIS with the statistical database currently available at SRID. • SRID and other MoFA STAFF trained in RS/GIS applications and remote sensing-based agricultural field survey techniques. • Land suitability analysis and land use planning • Rapid location of particular crops and livestock activities • Determination of onset and end of the rains • Rapid zonation of natural disasters (e.g. drought, floods) affecting agriculture • Identification of surface water resources • Rangeland monitoring and management • Pest threat monitoring (e.g. Locust)

10. APPROACH(METHOD) • IDENTIFICATION OF CROP TYPES AND AREA ESTIMATION • Studies will be conducted to map individual crop types and estimate crop areas using a combination of optical and microwave remote sensors (Landsat, SPOT, MODIS, NOAA , AVHRR, Radarsat, ERS, Envisat, JERS, Nigeria sat-X) • DEVELOPMENT OF CROP YIELD AND PRODUCTION MODEL • Multispectral, multi-temporal and/or multi-scale approaches that commonly use remote sensing information derived from satellites like SPOT, Landsat, NOAA-AVHRR, MODIS, Radarsat, ERS-1 would be used jointly with Agro-meteorological plant production models or statistical correlations to develop a Crop Yield and Production Model to predict yields of crops. • IDENTIFICATION OF CROP PHYSIOLOGICAL PARAMETERS FOR CROP GROWTH AND YIELD MODELLING • Crop Biophysical characteristics and physiological parameters derived from remotely sensed data would be selected to develop crop yield models. • FIELD TESTING, REFINEMENT AND VALIDATION OF MODEL • Yield simulation model established would be validated in the field based on its accuracy for crop classification and for retrieval of crop specific parameters form Remote Sensed data

11. PILOT STUDY LOCATION - GHANA • DISTRICTS WILL BE USED TO DESIGN, IMPLEMENT AND CALIBRATE A METHODOLOGY FOR REMOTE SENSING-BASED WATER RESOURCE AND CROP IDENTIFICATION AND AREA ESTIMATION • SELECTION OF PILOT AREAS ARE BASED ON AGRO-ECOLOGICAL ZONES OF GHANA

12. PROJECT SCHEDULE & COST • FIVE (5) YEARS @US$3,785,143

13. United Nations / Pakistan International Workshop Integrated Use of Space Technologies for Food- and Water Security Pakistan Space and Upper Atmosphere Research Commission (SUPARCO) 11 – 15 March 2013 Islamabad, Pakistan