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Showing items 1 through 9 of 208.
Has 3 main chapters: modes of access to land and natural resources and the tenure situation of the poor and marginalized groups (customary rules, statutory law, development of commercial transactions); some ways of securing land rights for the poor and other vulnerable groups (local resource mana
Ce Profil national genre des secteurs de l’agriculture et du développement rural a été préparé dans le cadre du projet de coopération technique de la FAO et de la Commission de la CEDEAO portant sur : « la Réponse Genre aux Plans Régionaux et Nationaux d’Investissement Agricole pour relever le dé
Augmenter l'utilisation des Directives volontaires pour la gouvernance responsable des régimes fonciers (DVGF) parmi les organisations de la société civile et de base – Mali est une fiche d'information sur le pays qui fournit des informations sur le projet «Augmenter l'utilisation des DVGF parmi
The land registration system in Kenya was established in 1897 to support land registration for white settlers who had come into the country during the 19th Century.
This research gives an evaluation of Tana delta with regard to areas that are suitable for rice growing. The study area lies on the Eastern delta area of the Tana river of which 16000 hectares have been earmarked for commercial rice farming.
Agriculture is the backbone of Kenya’s economy. Agriculture in Kenya is characterized by low productivity due to low external inputs, lack of good farming practices, soil erosion, and other losses.
The Earth’s land surface is a key component of its climate system. Terrestrial plants, animals and human beings rely on the land surface for sustenance and existence; as such, its prevailing conditions and properties are essential to terrestrial life.
Land use/cover changes are pervasive with no clear understanding of their spatial extends, drivers and impacts to society. Land-use changes have become a key component in the current strategies for managing and monitoring the natural resources and environment changes.
In this paper, we present an optimized Machine Learning (ML) algorithm for predicting land suitability for crop (sorghum) production, given soil properties information.
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