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The inland valley in Africa are common landscapes that have favorable conditions for agricultural production. Compared to the surrounding uplands they are characterized by a relatively high and secure water availability and high soil fertility levels. Inland valleys thus have a high agricultural production potential for crops such as rice, maize, yam, vegetables and fruits and they contribute to incoming generation and food security for the rural population. However, besides being important for agriculture, inland valleys provide essential ecosystem services such as water retention, flood control, erosion control and biodiversity conservation, amongst others. Inland valleys also provide communities with construction materials, grazing grounds for cattle, medicinal plants, hunting grounds, etc. Agricultural developments, promoted by national governments and international donors, must therefore be planned and implemented in a participatory way to assure sustainable use and exploitation of the resources among the users. In this lecture a four-step framework will be introduced that supports planning and implementation of sustainable inland valley development. In the framework tools are proposed for 1) selecting suitable inland valleys, 2) participatory land use planning, 3) design and implementation of water control and management, and 4) optimization of crop management practices. The the Smart-valleys approach for low-cost, participatory inland valley development of rice-based systems was developed by the Africa Rice Center. The implementation procedure and results that can be obtained will be presented as an alternative for high-cost – and often found not sustainable – irrigation infrastructure developments.

In the aftermath of the rice crisis in 2008 the Government of Senegal launched the program The Great Offensive for Food and Abundance running from 2008 to 2015. The goal was to achieve self-sufficiency for the major crops that are imported by making large investments in the agricultural sector aiming self-sufficiency by 2018 through intensification and area expansion incentives. The goal of this study was to analyze policy-induced trends in rice area, cultivation patterns and choice of varieties in the Senegal River Valley using advanced remote sensing and modelling techniques. We deployed the PhenoRice algorithm and time-series of 250 by 250 meter resolution MODIS satellite imagery between 2003 and 2016 to analyse cultivated areas in the dry and wet seasons, start and end of the rice season and length of the growing season. The results were validated with long term observations of farmer cultivation practices in two regions and with official statistics of rice cultivated areas. PhenoRice rice areas are reasonably correlated with the reported statistics (r2 = 0.71) and it was concluded that the algorithm is able to detected trends. However, the actual detected rice area is consistently lower than the reported areas. This was attributed to the mismatch between field size and pixel size as well as the fact that the schemes are relatively small and located in dry, desert-like environments. Before 2008 cropped areas during the dry season were minor (

Parasitic weeds invade around 1.34 million hectares of rainfed rice areas in Africa causing damages estimated at USD 200 million.

Rice is one of the major staple foods consumed in Africa and its demand continues to increase as a result of population growth, urbanization and changing diets. Mangrove rice cultivation is of importance along the West-African Atlantic coast from Senegal and Gambia down to Guinea-Bissau, Guinea-Conakry, Sierra Leone and Liberia. Although mangrove rice productivity is low it contributes for a major share to the regional rice production. Sea-level rise and reduction in river discharges, caused by the effects of climate change, lead to salt-water intrusion and are a potential threat to the mangrove rice production and regional food security. Information about rice areas is crucial to provide informed decision and management with the aim of safeguarding and improving rice production in those areas. However, till date such information is very limited or unavailable at all. Therefore our goal in this study was to map out rice cultivated areas within the mangrove ecosystem stretching from Senegal to Liberia and to characterize those systems in terms of altitude and the rice phenology using secondary data and spatial analysis. We deployed off-season Landsat 8 Images, 30 meters SRTM Digital elevation data, derived vegetation indices (EVI, SAVI, Tasseled cap Index Wetness) and Google Earth data. Decision tree classification was then applied using the DEM and Tasseled Cap index on Wetness to delineate the Landsat data into uplands and mangrove lowlands. Then, supervised classification using the ‘maximum likelihood’ classifier was applied on the delineated mangrove lowlands to characterize the area into rice and non-rice. The classification result was validated with Google Earth images. The second stage in this work was to investigate the rice phenology using MODIS NDVI time series. Overall, the study shows the potency of using medium resolution satellite images like Landsat for characterizing mangrove rice growing environment.

Purpose: Rice is an important staple crop cultivated in more than 163 million ha globally. Although information on the distribution of global rice production is available by country and, at times, at subnational level, information on its distribution within a year is often lacking in different rice growing regions. Knowing when and where rice is planted and harvested and the associated production is crucial to policy and decision making on food security. To examine seasonal and geographic variations in food supply, we developed a detailed rice crop calendar and linked it with disaggregated production data. Approach and methods used: We compiled from various sources detailed data on rice production, and planting and harvesting dates by growing season. To standardize the production data to the same period, we adjusted the production values so that the totals for each country will be the same as those of FAO for 2010-2012. We then linked data on rice production with the corresponding crop calendar information to estimate production at harvest time by month then we calculated totals for each country and region. Key results: The bulk of global annual harvests of rice is from September to November, corresponding with the harvest of the wet season rice in Asia and Africa. Total rough rice production during those peak months exceed 381 million tons, which account for about half of annual global rice output. Production is lowest in January with only 11 million tons in total. Regional production is lowest in Asia in January, Americas in December, Africa in July and rest of the world in May. Synthesis and Applications: A globally complete and spatially detailed rice crop calendar is important to crop growth simulation modelling and assessment of vulnerability of rice areas to biotic and abiotic stresses. Linked to production estimates, it can be used in analyzing spatial and seasonal production trends to better assess and predict price fluctuations , and to mitigate potential significant shortfalls in food production at certain times of the year.

Agriculture is the largest consumer and water. In the context of an increasing population and less water available for the agricultural sector, the water productivity needs to be sustained or increased to secure food security. This study provides benchmark values for water productivity for the major crops globally. Data were obtained from a literature review of measured water productivity values from field experiments, whereas remote sensing and modelling were used to assess the water productivity of wheat on a regional and on a global scale. The SEBAL model was applied to obtain spatially distributed values of water productivity in eight irrigated wheat systems globally. The analysis showed that the scope for improvement is significant in several of these systems in China, Pakistan and India. A new model, WATPRO, was developed that was applied on a global scale to benchmark water productivity of wheat at the beginning of the millennium.