Dr Anna Msigwa

Dr Anna Msigwa


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Work and Research

Agricultural water management

Fields Of Expertise

Hydrology and water resources
Agricultural water management


Revolutionizing water usage for the long and short term is what Dr Anna Msigwa does for a living. She studied the efficient use of water for agriculture in the Pangani basin in Tanzania, uniquely incorporating seasonal land-use dynamics into water analysis models. Her unique approach is already being used to study agricultural water used across Africa and globally by PhD students at the Vrije University of Brussels in Belgium.


At grassroots level, farmers have bought into the research implementing terraces for water and soil conservation. For her FAR-LeaF research, Dr Msigwa will study Remote sensing and machine Learning in water stress detection, with an emphasis on applications in maize farming.


Agriculture in Tanzania represents almost 30% of the country’s GDP, with three-quarters of its workforce involved in this sector. Yet, productivity is low with modest progress over the past two decades. The productivity of major crops such as maize and wheat is expected to fall this year: we expect post-harvest losses, below-average rainfall, pests, and high input prices. With a projected population growth of some 69,7 million people by 2050, the anticipated doubling of food demand cannot yet be met.


We need to reduce the amount of water used per unit yield by cutting yield loss – and the amount of water used for irrigation. To achieve this, the early detection and monitoring of plant responses to stress in crops are mandatory. Remote sensing will give us access to data as input for precision agriculture. This in turn promise to close the yield gap: optimizing food production using the right management practices at the right place and the right time while keeping the consumption of resources at an environmentally sustainable level, benefits ecosystems and human wellbeing, and the agricultural sector improves output, nutrition, and health.

With FAR-LeaF funding, we will develop a machine learning-based crop water stress mapping system using thermal infrared multi-/Hyperspectral remote sensing images and collected ground data in the Kikuletwa catchment area – 6077km2 within nine districts in the Arusha, Manyara and Kilimanjaro regions.


To monitor the water stress status of maize in-field, stem water potential (SWP) measurements will be captured using a customized pressure chamber. The SWP values will indicate whether crops are water-stressed – and targeted for irrigation.


Dr Msigwa’s research will lead to an application for water stress detection and the research results will give a mapped overview of the management practices, soil types and -characteristics, as well as up-to-date spatial and temporal climate variability in the catchment.


The project works closely with OIKOS, a Tanzanian NGO that promotes the protection of biodiversity and the sustainable use of natural resources as tools in the fight against climate change – the NGO will specifically help with communicating with farmers involved in the project.