Senegal

Background

Support agricultural production in irrigated rice systems

The 3PRD project (CSN1250) is a program implemented by the Société Nationale d'Aménagement et d'Exploitation des Terres du Delta du fleuve Sénégal et des vallées du fleuve Sénégal et de la Falémé (SAED), cofinanced by the Agence Française de Développement (AFD) with a contribution from the European Union, West African Development Bank (BOAD) and the Senegalese Government. 3PRD is a public-private investment model (shared between the State and the beneficiaries) to support the development of agricultural entrepreneurship involving family farms and local investors in rice production. Its aims are to increase national rice production and commercialization through (i) the irrigation of a new perimeter, (ii) the promotion of private investors to cultivate the perimeter, (iii) improving agricultural investments and access to credit, and (iv) support the rice value chain actors.

The project area is located in the community of "Diama" in the west of the Senegal River Valley. The irrigated agricultural landscape comprises a total surface of approximately 2,000 ha and is composed of more than 2,000 individual agricultural parcels, which have an average size of circa 1 hectare. For the project monitoring, two outcome indicators were mapped and quantified with satellite images: (i) the land-use specific area and (ii) a proxy for the rice productivity - each according to different land use categories - in four years (2017-2020).

Freely available, multi-temporal "Sentinel" 1/2 (Radar and optical images) satellite images from the Copernicus Earth observation program are used to assess the outcome indicators (Figure 1). First, to quantify the land-use area, machine learning algorithms were calibrated, based on in-situ collected ground observations of SAED, and the Sentinel 1/2 satellite images were classified. This output was multiple maps that show the cultivated area of different land use categories in four different years (2017-2020). Second, to proxy productivity without in-situ data, a so-called vegetation index was calculated, based on the Sentinel-2 satellite images. This vegetation index, called Normalized Difference Vegetation Index (NDVI), shows changes in productivity over the years. And third, in order to harness ground collected yield samples by SAED, a yield estimation model, based on satellite imagery in four years (2017-2020), was calibrated in addition.

Results

Using freely available satellite images and in-situ reference data to monitor crop area and productivity

The indicator maps entail useful spatial information about different land use categories and crop productivity in 4 consecutive years. They allow quantifying the land-use specific area (crop acreage) and its change over the observation period (2017-2020). The maps reveal that the area of more intensively used rice fields, which have two harvests per growing season, increased compared to the level in 2017 (see Figure 2 and 3).

However, some farmers tend to cultivate rice only during the first dry season because of natural hazards during the rainy season making farming unpredictable. Indeed, varying precipitation patterns, as well as pests and diseases more prevalent during the second season, make stable yields harder to achieve. As a result, farmers only partially adopted double cropping as a means of intensification for rice agriculture.

Next to proxying crop productivity with NDVI, with is a suitable approach in the absence of any in-situ measurements, a crop yield modelling approach by means of a Random Forest regression was implemented. In contrast to NDVI, this method actually quantifies the amount of rice crop yield, yet, it requires a comprehensive set of in-situ collected reference data about on ground measured crop yield. The multi-year rice yield model, which was calibrated with SAED ground data, enabled mapping the rice yield at the level of single field parcels (see Figure 4 at the example of late rice) allowing fine-scale comparative analysis between single plots or cooperatives. The conducted accuraccy assessment suggests, that we can confidently model unvisitied fields with an error slightly below 1 t/ha when a random sample of about 50 fields was available from the same year. However, applying the crop model for years it was not trained on leads to an unsatisfactory increase in the model error of above 2.5 t/ha.