L'agriculture de décrue au gré de la variabilité des politiques publiques sénégalaises

Introduction Sur les rives de la moyenne vallée du fleuve Sénégal, la culture du sorgho de décrue a assuré la base de la subsistance des populations pendant quelques millénaires (fig. 1). Or, dès l’indépendance, le gouvernement sénégalais a opté pour une politique de modernisation de l’agriculture qui prévoyait le remplacement de cette agriculture de décrue par une riziculture irriguée intensive. Après la construction de deux grands barrages régulateurs, à Manantali en amont et à Diama en ava..

Linking local production to urban demand : the emergence of small-scale milk processing units in Southern Senegal

International audienceAu Sénégal, l'apparition des petites unités laitières semble être un facteur important dans le développement d'un système local amélioré de production laitière. Pour aborder cette réflexion, un aperçu a été conduit en 2002 à Kolda, au sud du Sénégal. L'approche "filière" a été choisie pour évaluer les transferts physiques, les niveaux des prix, la gestion de la qualité et l'organisation économique du secteur. Les résultats prouvent que les quantités de lait récoltées par les petites unités de traitement se sont accrues de 21 250 litres en 1996 à 113 600 litres en 2001. Avec la concurrence du lait en poudre importé, le futur développement du système dépendra certainement de l'amélioration des niveaux de productivité mais également de la satisfaction des besoins du consommateur en termes de qualité et de prix. Plus d'attention devrait être prêtée à la qualité spécifique des produits locaux

BFASTm-L2, an unsupervised LULCC detection based on seasonal change detection – An application to large-scale land acquisitions in Senegal

In the context of Global Change Research, detection, monitoring and characterization of land use/land cover (LULC) changes are of prime importance. The increasing availability of dense satellite image time series (SITS) has led to a shift in the change detection paradigm, with algorithms able to exploit the full temporal information laid down in SITS. So far, most of these algorithms have focused on the detection of abrupt and gradual changes, and thus developed breakpoint detection based on significant deviations from the mean. However, LULC changes may manifest themselves in other patterns, particularly changes in seasonality (amplitude, number and length of the growing seasons) that are harder to detect. In this paper, we propose a simple method to automatically select the breakpoint linked to the biggest seasonal change in long and dense SITS with multiple breakpoints. This approach - BFASTm-L2 - relies on linking a high-speed algorithm (BFAST monitor) with a time series similarity metric (Euclidian distance L2) sensitive to seasonal changes. The capacity of BFASTm-L2 to identify the date of change in different situations was tested on two data sets, and compared to the performances of three other algorithms (BFAST monitor, BFAST lite, and Edyn). The data sets are 1. a published benchmark data set composed of 25 200 simulated SITS with different change types and change magnitudes, and 2. the 2000–2020 MODIS NDVI SITS over a 200x200 pixels area in Senegal including different study sites which have undergone recent LULC changes due to agricultural large-scale land acquisitions (LSLAs) (as reported in the ground field database used in this study). The results show that BFASTm-L2 is efficient in accurately detecting in time most of the changes, and, in contrast with BFAST Lite and BFASTmonitor, to spatially highlight LSLAs-induced changes without the need of any prior knowledge. The automatic proposed approach, faster than BFAST Lite and Edyn, and with very few tuneable parameters, may thus be easily implemented in unsupervised pipelines to map and analyse generic LULC changes at regional scale

Satellite-based detection of potential land grabs

International audienceLarge scale land acquisitions (LSLAs), often referred as “land grabbing”, are highly dynamic and complex land use systems that are rapidly transforming ecosystems and societies in many low-income countries of the world, bringing on one hand sustainability challenges and, on the other hand, undermining the right of peoples to self-determination over natural resources. As such, monitoring of those large-scale agricultural expansions has appeared to be of paramount importance. In response to that need, the Land Matrix international initiative has emerged to promote the creation of an open access database on world land transactions. This open tool enables the collection and visualization of data on land deals based on publicly available sources (i.e. from governments, corporations, medias, citizen). However, because information on those acquisitions is often opaque and scarce, systems allowing near real-time LSLAs detection, characterization and monitoring are needed. In this context, the increasing availability of free-of-cost global satellite data products has shown great potential for providing insights into land dynamics, particularly of large and remote areas. While LSLAs are not directly observable from remote sensing images (no one-to-one relation between land cover and functionality), they may be inferred from observable land cover and spatio-temporal characteristics at different scales, and structural elements in the landscape. At a pixel level, land use and land cover (LULC) changes are often detected using change detection algorithms applied on temporally-dense satellite image time series (SITS) of vegetation indices. So far, most of the LULC change studies have focused on forested land covers where significant deviations (anomalies) from the mean are relatively easy to detect. However, LULC changes, and in particular human-driven ones such as those induced by LSLAs, often imply a change in (seasonal) interannual patterns (not always with significant shifts from the mean), that are less well detected by change detection algorithms. Accurate and automatic detection of those type of changes would thus pave the way for the development of generic and unsupervised approaches to LULC change detection.This study deals with the detection of agricultural LSLAs under different environmental conditions. Focus is given on Senegal for which we have ground-truth data. In addition, its strong north-south gradient of rainfall from dry to semi-humid climate, and relatively small sizes of its deals make Senegal an interesting and difficult study case study for the detection of LSLAs. The detection method proposed here is based on a two-step approach: 1- the detection of (if any) breakpoints in dense MODIS 2000-2020 Vegetation Index (NDVI) time series using the very fast BFAST monitor algorithm. Because BFAST monitor algorithm is subject to a high false positive rate, we implemented a second step to select the breakpoint most likely related to the desired land use change (biggest pattern change), 2- the selection, for each pixel, of the breakpoint associated to the biggest phenological change, based on a time series distance computed between the subsamples before and after each breakpoint. Results consist of change-intensity maps, date-of-change maps, and a comparison of the change detection maps obtained using our method vs. using the biggest BFAST-magnitude change detected. Areas potentially related to agricultural LSLAs are identified and qualitatively/quantitatively characterized (e.g. year of change, spatial expansion) and evaluated against field data (when available) and high spatial resolution spatial imagery (Landsat/ Google Earth). The method was also tested over different more humid and forested specific areas found in the literature (e.g. Laos and Mozambique), where agricultural LSLAs have been reported and characterized. For these areas we produced maps of deforested areas, with associated date-of-change, that could be assessed qualitatively. The results indicate that our method has a high potential for detecting LSLAs even in humid regions, and thus for mapping the extent and dynamics of deforestation driven by different types of commodities. Future efforts will focus on a finer assessment of the driving factor of the detected LULC changes (e.g. fire, forest management, commodities cropping etc.) through the application of image analysis techniques (clustering and object-based image analysis)

Beyond controversy, putting a livestock footprint on the map of the Senegal River delta

International audienceThe Senegalese delta, like many other agricultural territories in the Global South, is experiencing changes in agricultural trajectory. These changes are related to the promotion of competitive and performance-based forms of agriculture. In a context of tense relations between farmers and herders, the quest for equitable access to land, which is a guarantee of peace, stability, and balanced economic and social development, is being called into question by the arrival of capital investors and new actors that are highly supported by the State. This situation raises questions about two important issues: (i) the challenge of the sustainable management of natural resources, especially land; and (ii) the socio-political stakes related to the fact that land is a sensitive resource, both politically and socially. The situation is exacerbated by the fact that dominant discourses are being built around representation of unused and available lands. The aim of this article is to address this controversy by questioning land-use planning processes and tools and underlining the reality depicted. We demonstrate that discourses around land availability are built upon sectoral visions that tend to overshadow the realities of land use. Indeed, livestock farming and particularly its mobile form (i.e., pastoralism) is rendered invisible by not being considered in the majority of land-use and agricultural policies. Through a participatory survey of campsites, we show that gathering basic information on livestock farming should not to be reduced to technical issues. Beyond that, we acknowledge that these land-use issues are rooted in sector-based and neoliberal visions of development. We conclude by discussing the importance of effective decentralization in financial and technical means and the development of systemic proficiency that goes beyond normative sectoral views to acknowledge and act on territorial development

Chapitre 9. L’agriculture de décrue a-t-elle un avenir au Sénégal ?

Introduction L’avenir de l’agriculture de décrue de la moyenne vallée du fleuve Sénégal a suscité de vifs débats depuis l’Indépendance. Alors que dans les plans initiaux d’aménagement de la vallée, elle était censée disparaître, elle est toujours pratiquée par des dizaines de milliers de producteurs quand la crue le permet (Bruckman, 2018). Mais l’avenir de cette pratique est toujours incertain. Les tenants de son maintien listent une série d’atouts, mais la majorité estime que la pratique es..

Chapitre 11. « Entre deux eaux » : l’agriculture de décrue face aux politiques transfrontalières dans la vallée du fleuve Sénégal

Introduction L’agriculture de décrue a régressé un peu partout à travers le monde, en raison de la régulation des fleuves par les barrages et de l’expansion des périmètres irrigués, et à cause de la baisse des pluies dans certaines régions comme l’Afrique de l’Ouest. Elle apparaît rarement dans les statistiques agricoles des États et de la FAO, ou dans les politiques agricoles nationales. Les années de grande sécheresse, il n’y a quasiment pas de production de décrue. Pour beaucoup, c’est une..

Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries

The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural censuses are often poorly georeferenced and crop types are difficult to interpret directly from satellite imagery. In this paper, we present a database made of 24 datasets collected in a standardized manner over nine sites within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative; the sites were spread over seven countries of the tropical belt, and the number of data collection years depended on the site (from 1 to 7 years between 2013 and 2020). These qualitycontrolled datasets are distinguished by in situ data collected at the field scale by local experts, with precise geographic coordinates, and following a common protocol. Altogether, the datasets completed 27 074 polygons (20 257 crops and 6817 noncrops, ranging from 748 plots in 2013 (one site visited) to 5515 in 2015 (six sites visited)) documented by detailed keywords. These datasets can be used to produce and validate agricultural land use maps in the tropics. They can also be used to assess the performances and robustness of classification methods of cropland and crop types/practices in a large range of tropical farming systems. The dataset is available at https://doi.org/10.18167/DVN1/P7OLAP (Jolivot et al., 2021)