Integrating weather and climate information into innovations and metrics for agricultural advisories

Background

Rainfed agriculture is a vital source of food and income for subsistence farmers in Kenya. Agricultural yields across Kenya are highly impacted by variations in rainfall on timescales from days to decades, with climate change altering the profile of rainfall-related risk. Providing warnings and advisories to agricultural stakeholders can enable farmers and agricultural extension workers to take actions to reduce the impact on crop yields. Understanding projected changes in agriculturally-relevant rainfall can directly inform adaptation strategies (e.g. crop variety).

In this PhD you will:

-conduct high-impact physical science research underpinning the development of decision-relevant agricultural advisories for Kenya,

-explore the meteorological drivers and predictability of rainfall throughout the wet seasons across Kenya (including associated uncertainty), on subseasonal to seasonal timescales,

-explore the design of advisories in the context of decision-making under changing climate

PhD opportunity

This PhD will be conducted alongside the iSPARK project, which is working with Kenyan organisations (KALRO, iSHAMBA) to improve existing agricultural advisories in West Kenya. Through engagement with iSPARK you will have the opportunity to work with researchers and local stakeholders in Kenya, exploring the delivery of decision-relevant agricultural interventions.

Firstly, you will design metrics that describe rainfall characteristics of relevance to agriculturalists. You will engage iSPARK staff (one will supervise the PhD) at Leeds and in Kenya, to understand the most relevant and useful rainfall information. These interactions, together with the literature, will allow you to identify target quantities to investigate, e.g. onset, length of dry spells, for which you will design metrics to quantify.

Multi-decadal timeseries and future climate projections will be used to explore recent and future changes and variability in these metrics, to assess evolving risk to agriculture in Kenya. This will bring changing risk context to advisories, which are currently focused on short-term risk.

Meteorological drivers of the target quantities will be explored, to understand the important modes of variability and potential predictability of such events at subseasonal to seasonal timescales. Using a range of state-of-the-art weather forecasts, the skill of forecasts of these metrics will be assessed, including quantification of uncertainty. There is potential to explore the use of data science and machine learning approaches to improve forecasts.

Through interaction with iSPARK, you will explore the design of advisories in the decision-making context, including how uncertainty in timing and amounts of rainfall impacts decision making. Your work will bring unique climate expertise alongside existing iSPARK expertise in agricultural advisories, remote sensing and crop modelling, and will bring quantification of uncertainty to iSPARK, and place it in the context of changing climates.

Other information