Nowadays, projects that invest in sustainable water management and agriculture require evidence that the targeted measures to boost water productivity are effective. Water productivity monitoring therefore becomes increasingly important. Water productivity requires data on yields and water consumption (evapotranspiration). Yield data are often difficult to obtain from farmers, especially in areas with many smallholders. Evapotranspiration is even more difficult to assess in the field. Remote sensing-based and model-based monitoring of water productivity has a large potential, also to identify yield gaps and assess the local feasible effectiveness of measures.

The objective of this pilot study was to achieve plot-level maps of water productivity and yield to test a methodology to assess the performance of different farmers in order to provide them with recommendations to improve water productivity. More specifically, this pilot study combined high-resolution imagery from Flying Sensors (FS) with a crop water productivity model to assess yield and water productivity for several plots with maize in Mozambique. Canopy cover was derived from the imagery and linked with the crop model simulations to obtain water productivity maps covering the entire growth cycle. The methodology is also used for the monitoring of crop performance during the growth season and can be used to forecast yield by the end of the season.

This feasibility study demonstrated that there is an opportunity to further develop a service that monitors water productivity based on FS-imagery and crop modelling. Service costs outweigh the additional revenues obtained by farmers. The experimental development has demonstrated that the service is technically feasible and can provide the tangible outputs needed. To bring the proposed service to a higher level of maturity, it is recommended to focus future development activities on (i) Testing for different locations and crops, (ii) Further enhancing link between FS-based imagery and crop modelling, and (iii) Involving end-users and testing within a project where WP-measures are implemented.

Publicaciones relacionadas

• 2017 - FutureWater Report 172Den Besten, N.I., J.E. Hunink, G.W.H. Simons. 2017. Water Productivity assessment using Flying Sensors and Crop Modelling: Pilot study for Maize in Mozambique. FutureWater Report 172.X

  Water Productivity assessment using Flying Sensors and Crop Modelling: Pilot study for Maize in Mozambique

  Den Besten, N.I., J.E. Hunink, G.W.H. Simons