Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa

This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. The analysis focuses on maize cultivation in Malawi and Ethiopia, and leverages rich, georeferenced plot-level data from national household surveys that were conducted in 2018–20 and integrated with Sentinel-2 satellite imagery and complementary geospatial data. To identify the approach to survey data collection that yields optimal data for training remote sensing models, 26,250 in silico experiments are simulated within a machine learning framework. The best model is then applied to map seasonal maize cultivation from 2016 to 2019 at 10-m resolution in both countries. The analysis reveals that smallholder plots with maize cultivation can be identified with up to 75% accuracy. Collecting full plot boundaries or complete plot corner points provides the best quality of information for model training. Classification performance peaks with slightly less than 60% of the training data. Seemingly little erosion in accuracy under less preferable approaches to georeferencing plots results in the total area under maize cultivation being overestimated by 0.16–0.47 million hectares (8–24%) in Malawi.

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Precision Agriculture for Resource Use Efficiency in Smallholder Farming Systems in Sub-Saharan Africa: A Systematic Review

Sustainability ◽

10.3390/su13031158 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1158

Author(s):

Cecilia M. Onyango ◽

Justine M. Nyaga ◽

Johanna Wetterlind ◽

Mats Söderström ◽

Kristin Piikki

Keyword(s):

Resource Use ◽

Precision Agriculture ◽

Large Scale ◽

English Language ◽

Management Practices ◽

Smallholder Farmers ◽

Management Strategies ◽

Sub Saharan Africa ◽

Smallholder Farming ◽

Sub Saharan

Opportunities exist for adoption of precision agriculture technologies in all parts of the world. The form of precision agriculture may vary from region to region depending on technologies available, knowledge levels and mindsets. The current review examined research articles in the English language on precision agriculture practices for increased productivity among smallholder farmers in Sub-Saharan Africa. A total of 7715 articles were retrieved and after screening 128 were reviewed. The results indicate that a number of precision agriculture technologies have been tested under SSA conditions and show promising results. The most promising precision agriculture technologies identified were the use of soil and plant sensors for nutrient and water management, as well as use of satellite imagery, GIS and crop-soil simulation models for site-specific management. These technologies have been shown to be crucial in attainment of appropriate management strategies in terms of efficiency and effectiveness of resource use in SSA. These technologies are important in supporting sustainable agricultural development. Most of these technologies are, however, at the experimental stage, with only South Africa having applied them mainly in large-scale commercial farms. It is concluded that increased precision in input and management practices among SSA smallholder farmers can significantly improve productivity even without extra use of inputs.

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Within-Season Changes in Land-Use Impact Pest Abundance in Smallholder African Cassava Production Systems

Insects ◽

10.3390/insects12030269 ◽

2021 ◽

Vol 12 (3) ◽

pp. 269 ◽

Cited By ~ 1

Author(s):

Andrew Kalyebi ◽

Sarina Macfadyen ◽

Andrew Hulthen ◽

Patrick Ocitti ◽

Frances Jacomb ◽

...

Keyword(s):

Land Use ◽

Bemisia Tabaci ◽

Large Scale ◽

Production Systems ◽

Smallholder Farmers ◽

Negative Relationship ◽

Central Africa ◽

Sub Saharan Africa ◽

Smallholder Farming ◽

Manihot Esculenta Crantz

Cassava (Manihot esculenta Crantz), an important commercial and food security crop in East and Central Africa, continues to be adversely affected by the whitefly Bemisia tabaci. In Uganda, changes in smallholder farming landscapes due to crop rotations can impact pest populations but how these changes affect pest outbreak risk is unknown. We investigated how seasonal changes in land-use have affected B. tabaci population dynamics and its parasitoids. We used a large-scale field experiment to standardize the focal field in terms of cassava age and cultivar, then measured how Bemisia populations responded to surrounding land-use change. Bemisia tabaci Sub-Saharan Africa 1 (SSA1) was identified using molecular diagnostics as the most prevalent species and the same species was also found on surrounding soybean, groundnut, and sesame crops. We found that an increase in the area of cassava in the 3–7-month age range in the landscape resulted in an increase in the abundance of the B. tabaci SSA1 on cassava. There was a negative relationship between the extent of non-crop vegetation in the landscape and parasitism of nymphs suggesting that these parasitoids do not rely on resources in the non-crop patches. The highest abundance of B. tabaci SSA1 nymphs in cassava fields occurred at times when landscapes had large areas of weeds, low to moderate areas of maize, and low areas of banana. Our results can guide the development of land-use strategies that smallholder farmers can employ to manage these pests.

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Development patterns under population pressure: Agricultural development and the cassava-livestock interaction in smallholder farming systems in Sub-Saharan Africa

Agricultural Systems ◽

10.1016/0308-521x(95)93645-t ◽

1995 ◽

Vol 48 (1) ◽

pp. 51-72 ◽

Cited By ~ 35

Author(s):

Luc Christiaensen ◽

Eric Tollens ◽

Chuma Ezedinma

Keyword(s):

Agricultural Development ◽

Farming Systems ◽

Sub Saharan Africa ◽

Population Pressure ◽

Smallholder Farming ◽

Development Patterns ◽

Smallholder Farming Systems ◽

Sub Saharan

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Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery

Drones ◽

10.3390/drones2030022 ◽

2018 ◽

Vol 2 (3) ◽

pp. 22 ◽

Cited By ~ 9

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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Implications of livestock feeding management on soil fertility in the smallholder farming systems of sub-Saharan Africa

Agriculture Ecosystems & Environment ◽

10.1016/s0167-8809(00)00244-9 ◽

2001 ◽

Vol 84 (3) ◽

pp. 227-243 ◽

Cited By ~ 42

Author(s):

R Delve

Keyword(s):

Soil Fertility ◽

Farming Systems ◽

Sub Saharan Africa ◽

Smallholder Farming ◽

Feeding Management ◽

Smallholder Farming Systems ◽

Livestock Feeding ◽

Sub Saharan

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

Climate Change and Multi-Dimensional Sustainability in African Agriculture ◽

10.1007/978-3-319-41238-2_33 ◽

2016 ◽

pp. 639-658 ◽

Cited By ~ 3

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

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Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

Remote Sensing ◽

10.3390/rs13224599 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4599

Author(s):

Félix Quinton ◽

Loic Landrieu

Keyword(s):

Time Series ◽

Deep Learning ◽

Crop Rotation ◽

Large Scale ◽

State Of The Art ◽

Crop Rotations ◽

Learning Approach ◽

Type Mapping ◽

Current State ◽

Crop Type

While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.

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Integrating active learning and crowdsourcing into large-scale supervised landcover mapping algorithms

10.7287/peerj.preprints.3004v1 ◽

2017 ◽

Cited By ~ 1

Author(s):

Stephanie R Debats ◽

Lyndon D Estes ◽

David R Thompson ◽

Kelly K Caylor

Keyword(s):

Active Learning ◽

Large Scale ◽

Learning Algorithm ◽

Training Data ◽

Sub Saharan Africa ◽

Data Sets ◽

Field Patterns ◽

Sub Saharan ◽

Highly Correlated ◽

Computational Resources

Sub-Saharan Africa and other developing regions of the world are dominated by smallholder farms, which are characterized by small, heterogeneous, and often indistinct field patterns. In previous work, we developed an algorithm for mapping both smallholder and commercial agricultural fields that includes efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. In this paper, we demonstrated how active learning can be incorporated in the algorithm to create smaller, more efficient training data sets, which reduced computational resources, minimized the need for humans to hand-label data, and boosted performance. We designed a patch-based uncertainty metric to drive the active learning framework, based on the regular grid of a crowdsourcing platform, and demonstrated how subject matter experts can be replaced with fleets of crowdsourcing workers. Our active learning algorithm achieved similar performance as an algorithm trained with randomly selected data, but with 62% less data samples.

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