Maize yield forecast using earth observation data and machine learning for Sub-Saharan Africa

2021 ◽  
Author(s):  
Donghoon Lee ◽  
Frank Davenport ◽  
Shraddhanand Shukla ◽  
Greg Husak ◽  
Chris Funk
Keyword(s):  
Machine Learning ◽  
Management Practices ◽  
Temporal Dynamics ◽  
Warning System ◽  
Maize Yield ◽  
Earth Observation ◽  
Sub Saharan Africa ◽  
Observation Data ◽  
Yield Forecast ◽  
Sub Saharan

<p>In Sub-Saharan Africa, forecasting of agricultural production is becoming increasingly important for the management of the agricultural supply chain, market prediction, and food aid. More importantly, agricultural forecasts can enhance the ability of governments and humanitarian organizations to respond better to food production shocks and price spikes caused by extreme droughts. Here, we use earth observation (EO) and machine learning (ML) techniques to develop 1-6 months ahead end-of-season maize yield forecast models for several regions in Sub-Saharan Africa. We find that ML models present different aspects of forecast accuracy compared to baseline regression models. Specifically, we investigate 1) skillful EO predictors and their predictability in a given region and lead-time and 2) the benefits of using finer time resolution of EO data that can potentially capture temporal dynamics in early reproductive stages. Overall, this study provides the groundwork for an operational crop yield forecast and famine warning system. Actionable famine risk predictions can radically improve existing disaster management practices of aid organizations by providing advanced preparedness and response strategies.</p>

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

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.

scholarly journals Modeling the temporal dynamics of cervicovaginal microbiota identifies targets that may promote reproductive health

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Alexander Munoz ◽  
Matthew R. Hayward ◽  
Seth M. Bloom ◽  
Muntsa Rocafort ◽  
Sinaye Ngcapu ◽  
...  
Keyword(s):  
High Risk ◽  
Bacterial Communities ◽  
High Probability ◽  
Temporal Dynamics ◽  
Transition Probabilities ◽  
Low Risk ◽  
Sub Saharan Africa ◽  
Reproductive Outcomes ◽  
Bacterial Composition ◽  
Sub Saharan

Abstract Background Cervicovaginal bacterial communities composed of diverse anaerobes with low Lactobacillus abundance are associated with poor reproductive outcomes such as preterm birth, infertility, cervicitis, and risk of sexually transmitted infections (STIs), including human immunodeficiency virus (HIV). Women in sub-Saharan Africa have a higher prevalence of these high-risk bacterial communities when compared to Western populations. However, the transition of cervicovaginal communities between high- and low-risk community states over time is not well described in African populations. Results We profiled the bacterial composition of 316 cervicovaginal swabs collected at 3-month intervals from 88 healthy young Black South African women with a median follow-up of 9 months per participant and developed a Markov-based model of transition dynamics that accurately predicted bacterial composition within a broader cross-sectional cohort. We found that Lactobacillus iners-dominant, but not Lactobacillus crispatus-dominant, communities have a high probability of transitioning to high-risk states. Simulating clinical interventions by manipulating the underlying transition probabilities, our model predicts that the population prevalence of low-risk microbial communities could most effectively be increased by manipulating the movement between L. iners- and L. crispatus-dominant communities. Conclusions The Markov model we present here indicates that L. iners-dominant communities have a high probability of transitioning to higher-risk states. We additionally identify transitions to target to increase the prevalence of L. crispatus-dominant communities. These findings may help guide future intervention strategies targeted at reducing bacteria-associated adverse reproductive outcomes among women living in sub-Saharan Africa.

scholarly journals Adoption and Dis-Adoption of Sustainable Agriculture: A Case of Farmers’ Innovations and Integrated Fruit Fly Management in Kenya

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 338
Author(s):  
Charity M. Wangithi ◽  
Beatrice W. Muriithi ◽  
Raphael Belmin
Keyword(s):  
Management Practices ◽  
Household Head ◽  
Fruit Fly ◽  
Production Costs ◽  
Sub Saharan Africa ◽  
Bactrocera Dorsalis ◽  
Major Threat ◽  
Gender And Education ◽  
Sub Saharan ◽  
Indigenous Methods

The invasive fruit fly Bactrocera dorsalis poses a major threat to the production and trade of mango in sub-Saharan Africa. Farmers devise different innovations to manage the pest in an attempt to minimize yield loss and production costs while maximizing revenues. Using survey data obtained from Embu County, Kenya, we analyzed farmers’ knowledge and perception as regards the invasive fruit fly, their innovations for the management of the pest, and the determinants of their adoption and dis-adoption decisions of recently developed and promoted integrated pest management (IPM) technologies for suppression of the pest. The results show that farmers consider fruit flies as a major threat to mango production (99%) and primarily depend on pesticides (90%) for the management of the pest. Some farmers (35%) however use indigenous methods to manage the pest. Though farmers possess good knowledge of different IPM strategies, uptake is relatively low. The regression estimates show that continued use of IPM is positively associated with the gender and education of the household head, size of a mango orchard, knowledge on mango pests, training, contact with an extension officer, and use of at least one non-pesticide practice for fruit fly management, while IPM dis-adoption was negatively correlated with the size of the mango orchard, practice score and use of indigenous innovations for fruit fly management. We recommend enhancing farmer′s knowledge through increased access to training programs and extension services for enhanced adoption of sustainable management practices for B. dorsalis.

scholarly journals Closing maize yield gaps in sub-Saharan Africa will boost soil N2O emissions

2020 ◽  
Vol 47 ◽  
pp. 95-105 ◽  
Author(s):  
Sonja Leitner ◽  
David E Pelster ◽  
Christian Werner ◽  
Lutz Merbold ◽  
Elizabeth M Baggs ◽  
...  
Keyword(s):  
Maize Yield ◽  
Sub Saharan Africa ◽  
N2o Emissions ◽  
Yield Gaps ◽  
Sub Saharan

scholarly journals Implications of Climate Change on Soil Functions: A Case for Achieving Local Coconut Sufficiency from Sustainable Agricultural Practices

2021 ◽  
Author(s):  
Chinedu Egbunike ◽  
Nonso Okoye ◽  
Okoroji-Nma Okechukwu
Keyword(s):  
Climate Change ◽  
Management Practices ◽  
Negative Impact ◽  
Agricultural Practices ◽  
Sub Saharan Africa ◽  
Soil Functions ◽  
Soil Biodiversity ◽  
Environmental Consequences ◽  
Sub Saharan ◽  
Sustainable Agricultural Practices

Climate change is a major threat to agricultural food production globally and locally. It poses both direct and indirect effects on soil functions. Thus, agricultural management practices has evolved to adaptation strategies in order to mitigate the risks and threats from climate change. The study concludes with a recommendation the coconut farmers should explore the idea of soil biodiversity in a bid to mitigate the potential negative impact of climate related risk on the farming. The study proffers the need for adopting sustainable agricultural practices to boost local coconut production. This can contribute to the simultaneous realisation of two of the Sustainable Development Goals (SDGs) of the United Nations: SDG 2 on food security and sustainable agriculture and SDG 13 on action to combat climate change and its impacts. The study findings has implications for tackling climate change in Sub-Saharan Africa and in particular Nigeria in order to boost local agricultural production and coconut in particular without negative environmental consequences and an ability to cope with climate change related risks.

scholarly journals Earth Observation Data for Agricultural Drought Monitoring in the Pannonian Basin

2020 ◽  
Author(s):  
Laura Crocetti ◽  
Milan Fischer ◽  
Matthias Forkel ◽  
Aleš Grlj ◽  
Wai-Tim Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Pannonian Basin ◽  
Spatial Scales ◽  
European Space Agency ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Observation Data

<p>The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.</p><p>Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25°) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.</p><p>This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).</p>

A Land Management Based Approach to Integrated Striga hermonthica Control in sub-Saharan Africa

1996 ◽  
Vol 25 (3) ◽  
pp. 157-164 ◽  
Author(s):  
Dana Berner ◽  
Robert Carsky ◽  
Kenton Dashiell ◽  
Jennifer Kling ◽  
Victor Manyong
Keyword(s):  
Land Management ◽  
Management Practices ◽  
Farming Systems ◽  
Integrated Approach ◽  
Cultural Control ◽  
Crop Rotations ◽  
Sub Saharan Africa ◽  
Striga Hermonthica ◽  
Bush Fallow ◽  
Sub Saharan

Striga hermonthica, an obligate root parasite of grasses, Is one of the most severe constraints to cereal production in sub-Saharan Africa. In the recent past, prior to increased production pressure on land, S. hermonthica was controlled in African farming systems by prolonged crop rotations with bush fallow. Because of increasing need for food and concomitant changes in land management practices, however, these fallow rotations are no longer extensively used. Shorter crop rotations and fallow periods have also led to declines in soil fertility which present a very serious threat to African food production. A sustainable solution will be an integrated approach that simultaneously addresses both of these major problems. An integrated programme that replaces traditional bush fallow rotation with non-host nitrogen-fixing legume rotations, using cultivars selected for efficacy in germinating S. hermonthica seeds, is outlined. The programme includes use of S. hermonthlca-free planting material, biological control, cultural control to enhance biological suppressiveness, host-plant resistance, and host-seed treatments.

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