SADC Agricultural Information Management System Crop and Rangeland

SADC Agricultural Information Management System: Crop and Rangeland Monitoring Activities for Early Warning for Food Security Blessing Siwela, Agricultural Information Officer, SADC FANR

Outline • Background to SADC Region • About SADC FANR, Agricultural Information Management System (AIMS) • Crop and Rangeland Monitoring approach • Data / information gathering mechanisms • Use of Earth Observation data for season crop and rangeland monitoring • Main indicators of crop condition used • Information Dissemination approaches • Challenges • Upcoming developments

SADC Region - Background Southern African Development Community § § § § § 15 Member States 220+ million people. Varied climate regions. Mostly uni-modal rainfall systems (bimodal in the north). Varied cropping systems. Cereal cropping dominant (Maize, sorghum, millet, wheat) Cassava and tubers important in the north. Rain fed agriculture – irrigation only significant in South Africa and Zimbabwe. Main Livestock types include cattle, goats, sheep

Agricultural Information Management System (AIMS) • The Agricultural Information Management System (AIMS) programme is meant to provide planners and policy makers easy access to information necessary for revitalizing agricultural and natural resources growth, enhancing food security and promoting rural development. • Activities include: – development of networks for timely collection of information for early warning, – vulnerability assessments, – food security assessments, – establishment of a Food, Agriculture and Natural Resources’ integrated database on food security and natural resources

AIMS: Operational Activities • Supporting agro-meteorologists in the use of satellite imagery products and GIS for crop and rangeland for food security. • Monitoring crops, vegetation and weather developments during the crop growing period using satellite images and GIS techniques. • Collection of information on agriculture (crop and livestock performance) from national contacts • Preparing and disseminating reports on the status of the main crop growing season, and the food security situation in SADC countries

AIMS: Operational Activities USGS/ VITO FEWSNET AMESD E-mail / internet • Acquisition and distribution of remote sensing datasets used for crop and rangeland monitoring Website SADC Archive Image Review / analysis Bulletins Country Windows E-mail Countries

Crop and Rangeland Monitoring approach § Identify the key cropland rangeland areas for monitoring § Collection of information from National Early Warning Systems § Climate / weather outlooks, Rainfall performance § Crop stages, crop and pasture condition § Hydrological information § Complement the information with remote sensing data § Use convergence of evidence to analyse and arrive at conclusions on the state of crops and rangeland § Identify “hot spots” for close monitoring § Prepare regular reports and distribute widely

Crop and Rangeland Monitoring approach • key cropland rangeland areas for monitoring identified through analysis of – historical yield and production information – livelihood analysis profiles FAO FEWSNET

Crop and Rangeland Monitoring approach • Collection of information from National Early Warning Systems – – – Climate / weather outlooks, Rainfall performance Farming inputs (machinery, fertilisers, seeds, etc) Crop stages, crop and pasture condition Livestock numbers and health Hydrological information Crop production estimates

Crop and Rangeland Monitoring approach • Collection of information from National Early Warning Systems - How? – Bulletins are received from early warning systems via email [10 -day agrometeorological updates, monthly food security reports] – Questionnaires (hardcopy and electronic) send out to contact points – Information exchange by email, telephone – Online forms for data collection are being introduced NEWU

Available Remote Sensing Data • Available satellite-based data used for regular monitoring: – vegetation products (NDVI from SPOT VEGETATION, NOAA AVHRR, MSG) – rainfall monitoring (CPC RFE 2. 0, TAMSAT RFE) • These products are analyzed and further processed into application specific products for flood and drought monitoring • Other datasets • MODIS NDVI • Vgt 4 Africa datasets (NDVI, DMP, NDWI, SWB, etc) • LANDSAT • ASTER

Applications of CPC RFE data • RFE is used to drive a number of applications used for agricultural season monitoring • These include – Rainfall performance – Water Requirements Satisfaction Index (WRSI) – Onset of Rains – Soil Moisture Index – Quelea birds breeding forecasts

Monitoring Rainfall Activity • CPC Rainfall Estimate (RFE 2. 0) data, which combines satellite images with rain gauge observations. • RFE images from USGS / FEWSNET January 2008 rainfall estimates Jan 1 -10 Jan 11 -20 Jan 21 -31 Zambia, 2008 Mozambique, 2008

Monitoring Rainfall Activity • Time-series graphs • Comparison with reference data (e. g. Averages) • >>Image differencing • >>Percentage maps Time series rainfall analysis Percentage cumulative rainfall received

Crop Condition Monitoring • The Water Requirements Satisfaction Index (WRSI) is a crop specific water balance approach that models the effect of seasonal rainfall availability on potential crop yields. • WRSI indicates the extent (in percent) to which the water requirements of the crop has been satisfied in a cumulative way at any stage of the crop growing season. • Two approaches are used in the SADC region – using satellite-based, distributed approach, and a ground-based point-specific approach • The model is being used in several SADC countries to monitor crop water use with a view to yield forecasting and estimation. SADC RRSU is providing training • Operational model run at USGS but modern modelling software now publicly available from FAO (Agromet. Shell) and USGS (Geo. Spatial WRSI).

Water Requirements Satisfaction Index Crop Water Balance Modeling Water Requirements Satisfaction Index (WRSI) WRSI=100*AET/WR Regression models Yield Estimation

Water Requirements Satisfaction Index Malawi WRSI yield modelling • WRSI yield models are used operationally to provide early estimates of yield and production • Higher correlations with yield in the southern parts of the country • Lower correlations in the northern districts could be related to poor distribution of rainfall stations WRSI / Yield R-Sq values Main maize production areas [FEWSNET]

Water Requirements Satisfaction Index Swaziland WRSI yield modelling • Swaziland yield models and their reliability • Correlations with WRSI high in the main maize growing areas • Poor correlations in Lowveld WRSI / Yield R-Sq values Main maize production areas [FEWSNET]

Monitoring of Pests : Quelea birds breeding forecasts • Forecasting suitable breeding conditions for the red-billed quelea birds • Based on daily rainfall (currently NOAA CPC estimates) and ancillary information on quelea birds • Updated weekly • Threats to small grain crops • Developed by Natural Resources Institute of the University of Greenwich, www. nri. org http: //www. sadc. int/fanr/aims/rrsu/quel/index. htm

Vegetation Monitoring Available NDVI datasets Type Resolution Frequency Source NOAA AVHRR 1. 1 km, 8 km 10 days USGS/FEWSNET SPOT VGT 1. 1 km 10 days VITO MODIS 250 m, 500 m 16 days USGS METEOSAT-8 3 km BMS Receiver

Vegetation Monitoring Available NDVI datasets GAC 8 km Source: USGS / FEWSNET 1982 -current 21 -31 January 2006 S-10 NDVI (MVC) from FAO ARTEMIS (via FTP) SADC-wide coverage Eumet. Cast distribution via MSG receivers, http: //www. vgt 4 africa. org

Vegetation Monitoring NDVI time-series analysis Time series analysis done for visualizing seasonal trends in the major agricultural areas Comparison with average and other seasons

Vegetation Monitoring NDVI trend series smoothing (Reed et al, 1999) February 11 -20 2000 Weighted least squares approach to NDVI smoothing 5 -dekad overlapping moving windows used February 21 -29 2000 March 1 -10 2000

Vegetation Monitoring 2003 drought in parts of southern Africa VGT S-10 NDVI used in SADC report to show extent of drought S P O T V G T

Vegetation Monitoring NDVI Based Indicators Vegetation Productivity Indicator (VPI) references current the NDVI value to its historical probability Developed at Cranfield University 20% 40% 60% 80%

Information Products • A number of bulletins are produced to meet information requirements, including: – Regular agro-meteorological updates at 10 -day and monthly intervals – Ad-hoc “Significant Weather Developments” (SWD) bulletins which aim to “ provide timely highlights of developing weather patterns and their potential impacts to human lives and property” – Other special bulletins to address current or issues e. g. Rainfall forecast interpretation; flood / drought alert • E-mail distribution • Websites: • www. sadc. int/fanr/aims • www. sadc. int/geonetwork/

Information Products • AIMS online data visualisation tools – Crop production [national and provincial]

Information Products • AIMS online data visualisation tools – Livestock health and numbers

Information Products • AIMS online data visualisation tools – Populations vulnerable to food security

Information Products – Country Profiles • AIMS online data visualisation tools – Country profiles

Information Products – Country Profiles • AIMS online data visualisation tools – Country profiles

Agric Areas Rainfall Crops Models Agromet Up-dates Agric Activities

2008 -2009 Agriculture Season Jan 1 – Feb 10, 2009 Rainfall, Percentage of average Consistent high rainfall totals in southern Angola and northern Namibia resulted in abnormally high river levels in the Cuvelai and Zambezi basin, causing extensive flooding. The flooding led to loss of lives, displacements of people and cattle, and also destroyed crops. Dry January conditions suggest a pre-mature cessation of short rains (Vuli) in the bi-modal rainfall areas of Tanzania. 2009 rainfall has been below average in eastern Zimbabwe, and central and southern Mozambique, dry spells in these Good crop conditions areas have resulted in moisture reported in South African stress. high maize production areas.

Significant Weather Forecast Cyclone Developments Tracks Major River Basins

Examples from SWD bulletins

Online Data Management Tools • SADC Geo. Network, www. sadc. int/geonetwork/ • A web based open-source metadata catalogue application • ISO 19115/19139 Geographic Metadata standards • Geo. Network integrates • – Search functions – Administrative functions – Data sharing - distribution and publication Developed by UN's FAO, WFP and UNEP to promote data sharing

Current main challenges • Lack of resources for monitoring of agriculture by extension officers • Ground rainfall observation networks and Data management – Awareness among decision makers on importance of these networks – Training of meteorological officers – Maintenance • Reliability of rainfall estimates • Identification of cropped areas • Crop area estimation for food security analysis • Availability software for analysis of early warning remote sensing and GIS datasets

Identified Areas of Need / Concern Agricultural Production Information Natural Resources Management Water bodies monitoring Soil types and Fertility Rainfall amount and Wetland status patterns Crop Area Measurement Land Management Information Deforestation / afforestation Yield Estimate Alien Species Invasion Crop Cycles Grazing Capacity Disaster Monitoring Seasonal biomass monitoring Classification of potential land Drought / Flood uses Bush Fires Demarcation of crop and Cyclones range land Pest and Diseases

Upcoming Initiatives • Collaboration with African Monitoring of the Environment for Sustainable Development (AMESD) on preparation of agro-meteorological reports – Theme: Agricultural and Environment Resource Management – Access to EO datasets – Information management aimed at improving decision making

Contacts For more information on the AIMS programme, contact – Blessing Siwela, bsiwela@sadc. int – Bentry Chaura, bchaura@sadc. int Website: www. sadc. int/fanr/aims Thank You