Search results | Land Portal | Securing Land Rights Through Open Data

Land Library Search

Through our robust search engine, you can search for any item of the over 50,000 highly curated resources in the Land Library. 

If you would like to find an overview of what is possible, feel free to peruse the Search Guide

Search results

Showing items 1 through 9 of 4.
  1. Library Resource
    Reports & Research
    January, 2016

    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. The purpose of this study was to assess the land covers change and decline in sugarcane farming using a three time series of Landsat satellite images of 1984, 2000 and 2015.

  2. Library Resource
    Journal Articles & Books
    April, 2015

    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. In most farming regions of the country, agriculture depends entirely on rainfall which sometimes is scarce. The problem of selecting the correct land for the cultivation of certain crops is a long-standing and mainly empirical issue. The objective of this study is to extrapolate and generate a crop suitability map showing areas suitable for agricultural activities in Taita Hills in Kenya.

  3. Library Resource
    Journal Articles & Books
    July, 2011

    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. The evaluation of land in terms of the suitability classes was based on the method as described in FAO guideline for land evaluation for rain fed agriculture. A land unit resulting from the overlay process of the selected theme layers has unique information of land qualities for which the suitability was based on.

  4. Library Resource
    Journal Articles & Books
    November, 2017

    In this paper, we present an optimized Machine Learning (ML) algorithm for predicting land suitability for crop (sorghum) production, given soil properties information. We set-up experiments using Parallel Random Forest (PRF), Linear Regression (LR), Linear Discriminant Analysis (LDA), KNN, Gaussian Naïve Bayesian (GNB) and Support Vector Machine (SVM). Experiments were evaluated using 10 cross fold validation. We observed that, parallel random forest had a better accuracy of 0.96 and time of execution of 1.7 sec. Agriculture is the main stream of food security.

Share this page