Defining climatic factors driving farming system decisions in Senegal

Period of Implementations

Oct 01, 2021 - Dec 15, 2021

Total budget

136,000

About

The up and out scaling of a technological innovation are driven by its suitability for specific farming system. These systems are defined by a complex interaction of climatic factors, and socio-economic considerations. Hence, in the optic of enhancing digital services to manage and reduce the impact of variable weather and extreme events, it is then critical to define these farming systems and their geographical distribution. In turns, such an information can later be used to inform policies, programming and investment decisions. The decision of adopting a given farming system in the Sahel is mostly driven by the climatic conditions affecting the farm, with the availability of moisture at specific times being a primary factor influencing this decision. Therefore, mapping the climatic factors affecting the cultivation of crops is a reliable proxy to predict the distribution of farming systems, especially if these are then linked to socio-economic considerations and validated on the ground at community level. This evidence-based approach can then be used to help determine the climate services that would benefit farmers and farming systems (including businesses, and other food and land system actors) to strengthen their resilience and adaptive capacity to climate-related hazards and natural disasters.

Impact

Goals

Defining the climatic factors affecting the cultivation of two crops, and then map these factors based on downscaled climatic datasets, to ultimately define the farming systems matching the main production types.

Objectives

1.1.1 Recovery in a centralized repository the available agronomic dataset for rice and wheat from the major research stations in Senegal from various institutions; 1.1.2 Recovery in a centralized repository the climatic and soil dataset for Senegal from various institutions; 1.1.3 Define additional potential sites needed to improve climatic modeling; 1.1.4 Data analysis by multiple regression to define climatic factors affecting the crop performances, and the suitability boundaries; 1.2.1 Geographical mapping of the climatic suitability boundaries to define the distribution of the farming systems; 1.3.1 Define 20 random GPS locations within these defined farming systems to conduct visits for validations ; 1.3.2 As part of these visits, define the socio-economic and management conditions of the local communities; 1.4.1 Plan with these communities sets of innovations for testing and validation;

Partners