Diseases and pests impacts on crop production under climate change in Nigeria-combining remote sensing and agro-Ecosystem modelling.
Shupel is a PhD Student at Geographic department of Humboldt univesity, Berlin, Germany, and Leibniz center for agricultural landscape research (ZALF). Her background is Geoinformation and earth observation.
About the project
Distinguished studies have brought to focus that agriculture in many developing countries would be more sensitive to climate change than in the developed nations. Some of which have underlined Africa as one of the most vulnerable continents to climate change. Unfortunately, large parts of the world where crop productivity is expected to decline under climate change also coincide with countries that currently have a high burden of hunger and high human population. This implies that, climate change will exacerbate food insecurity in areas that presently have a prevalence of hunger and under-nutrition. In many regions of Africa, severe cases of pest and crop diseases are linked to the negative impacts of climate change such as rising average temperatures and changes in precipitation regimes. Pests and diseases are two important factors that may depress the maximum achievable yields through different mechanisms. This has exacerbated damages in agricultural systems at all production levels. The problems are prominent in Nigeria, where rainfed subsistence farming dominates and crop systems are largely affected by a slight alteration in climate variables. Pest and diseases can exasperate hunger and poverty, as poor rural citizens in Nigeria are responsible for food production, with only a few scattered large holder farms owned by multinational cooperations. Using advanced remote sensing and crop modeling techniques, related risks may be mitigated in the future, but a deep understanding of the mechanisms behind crop diseases or operational early-warning systems are not in place to date. There is a need to explore high-medium multispectral data to develop methodologies of detecting and mapping crops affected by critical pest and disease as offered here. Taking advantage of open-sourced remote sensing data (Sentinel and Landsat) and machine learning methods. In another approach, the improvement of pest and disease models and their applicability to assess yield losses due to climate change is still a challenge for the scientific community and requires broadening. The above mentioned gaps have motivated this PhD research and necessitates a deeper understanding of the extent to which climate change and other environmental factors affects the severity of pest and diseases and subsequently crop yield. The specific goals of her PhD research are:
- Use robust automated remote sensing techniques to identify maize and potato fields in Nigeria’s Plateau State
- Develop a suitable remote sensing methodology to detect and map the spatial distribution of acute pest and disease infestations on these crops
- Using climate, physical and multisource remote sensing data and approaches to characterize areas with high risk of pest and crop diseases
- Advance agroecosystem crop modeling to adequately understand yield response to pest and crop disease as a result of climate change
Location of education
HU Berlin and ZALF
From Jun 2019 to Jun 2023
- Primary professor: Prof. Patrick Hostert (HU-Berlin)
- First supervisor: Dr. Claas Nendel (ZALF)
- Second advisor: Dr. Bahareh Kamali (ZALF)
German Academic Exchange Service (DAAD) and