Biological Inoculants for Sustainable Intensification of Agriculture in Sub-Saharan Africa Smallholder Farming Systems

2016 ◽  
pp. 639-658 ◽  
Author(s):  
C. Masso ◽  
R. W. Mukhongo ◽  
M. Thuita ◽  
R. Abaidoo ◽  
J. Ulzen ◽  
...  
Keyword(s):  
Farming Systems ◽  
Sustainable Intensification ◽  
Sub Saharan Africa ◽  
Smallholder Farming ◽  
Smallholder Farming Systems ◽  
Sub Saharan ◽  
Biological Inoculants

Phosphorus in smallholder farming systems of sub-Saharan Africa: implications for agricultural intensification

2015 ◽  
Vol 104 (3) ◽  
pp. 321-340 ◽  
Author(s):  
Generose Nziguheba ◽  
Shamie Zingore ◽  
Job Kihara ◽  
Roel Merckx ◽  
Samuel Njoroge ◽  
...  
Keyword(s):  
Agricultural Intensification ◽  
Farming Systems ◽  
Sub Saharan Africa ◽  
Smallholder Farming ◽  
Smallholder Farming Systems ◽  
Sub Saharan

scholarly journals Scaling agricultural mechanization services in smallholder farming systems: Case studies from sub-Saharan Africa, South Asia, and Latin America

2020 ◽  
Vol 180 ◽  
pp. 102792 ◽  
Author(s):  
Jelle Van Loon ◽  
Lennart Woltering ◽  
Timothy J. Krupnik ◽  
Frédéric Baudron ◽  
Maria Boa ◽  
...  
Keyword(s):  
Latin America ◽  
South Asia ◽  
Case Studies ◽  
Farming Systems ◽  
Sub Saharan Africa ◽  
Smallholder Farming ◽  
Smallholder Farming Systems ◽  
Agricultural Mechanization ◽  
Sub Saharan

scholarly journals Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery

Drones ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 22 ◽  
Author(s):  
Ola Hall ◽  
Sigrun Dahlin ◽  
Håkan Marstorp ◽  
Maria Archila Bustos ◽  
Ingrid Öborn ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Farming Systems ◽  
Sub Saharan Africa ◽  
Field Size ◽  
Image Texture ◽  
Great Promise ◽  
Smallholder Farming ◽  
Vegetation Fraction ◽  
Smallholder Farming Systems

Yield estimates and yield gap analysis are important for identifying poor agricultural productivity. Remote sensing holds great promise for measuring yield and thus determining yield gaps. Farming systems in sub-Saharan Africa (SSA) are commonly characterized by small field size, intercropping, different crop species with similar phenologies, and sometimes high cloud frequency during the growing season, all of which pose real challenges to remote sensing. Here, an unmanned aerial vehicle (UAV) system based on a quadcopter equipped with two consumer-grade cameras was used for the delineation and classification of maize plants on smallholder farms in Ghana. Object-oriented image classification methods were applied to the imagery, combined with measures of image texture and intensity, hue, and saturation (IHS), in order to achieve delineation. It was found that the inclusion of a near-infrared (NIR) channel and red–green–blue (RGB) spectra, in combination with texture or IHS, increased the classification accuracy for both single and mosaic images to above 94%. Thus, the system proved suitable for delineating and classifying maize using RGB and NIR imagery and calculating the vegetation fraction, an important parameter in producing yield estimates for heterogeneous smallholder farming systems.

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