Work Package 1: Assisting water projects
The objective of Work Package 1 (WP1) is to guide water projects and programs in understanding the concept of water productivity, to be able to report on trends in water productivity, identify causes for variability and decline in water productivity, and to be able to select a course of action for improving water productivity. All of these efforts will work towards the target of increasing water productivity by 25% as stipulated by the Dutch governmental agenda. The definition of water productivity used here is broad, and includes biophysical and socioeconomic parameters.
The aim is to develop standardized protocols for analyzing biophysical water productivity (e.g., using WaPOR or AquaCrop), to complete diagnostic analyses to identify the causes for low water productivity, and to identify and discuss measures to improve water productivity with (agricultural) water managers and users. These will be tested and implemented in selected case studies. For case studies where clear recommendations can be provided, the project team will work with the project leads and system managers to work towards implementing these recommendations.
Activities and Outputs
The user need assessment aimed to (1) determine potential partners in the co-development process and potential users of the service, and (2) to identify the major themes, information needs and capabilities in Kenya, Sudan and Ethiopia. An executive summary is available here
Standardized protocol for diagnostic analysis for water productivity variations
This protocol focuses on analyses that can be conducted to explain the reasons behind spatial and temporal biophysical water productivity (BWP) variations.The first aim of this protocol is to delineate the agronomic principles that affect water productivity (section 2). Second, this protocol aims to show how to make use of various existing tools that can provide diagnostic insights (section 3). The report can be found here, as well as the supporting excel files for field surveys and data collection for Aquacrop.
An operational framework was developed to predict field crop biomass using high resolution multi-source satellite data. Five regression algorithms were tested to assess their suitability to predict field scale sugarcane biomass production in the Wonji-Shoa estate, Ethiopia. The results showed that linear regression models outperformed non-linear machine learning models with 89% accuracy achieved 4 months before harvest. The full paper can be accessed here .
Translating open-source remote sensing data to crop water productivity improvement actions
This study presents a framework to translate RS based WP data to actionable information. Six factors (crop water stress, irrigation uniformity, soil salinity, nitrogen application, crop rotation and soil type) derived from RS were analysed to identify their influence on WP and yield. Such information with regard to WP factors assists practitioners to identify priority areas and actions aiming at crop field level WP improvement. Read more here.
This study presents a framework that applies WaPOR data to assess irrigation performance indicators including uniformity, equity, adequacy and land and water productivity differentiated by irrigation method (furrow, sprinkler and centre pivot) at the Xinavane sugarcane estate, Mozambique. You can view the article on the HESS webpage.
Standardized Protocol for Land and Water Productivity Analyses using WaPOR
This standardized protocol provides Python scripts which can be used to calculate land and water productivity and other performance indicators such as uniformity, efficiency (beneficial fraction), adequacy, relative water deficit as well as estimating productivity gaps. Go to the Github repository to view and download the Jupyter Notebooks with the protocol. For a short overview of the protocol check this two-page brief.
Case Study: Xinavane, Mozambique
The objective of this case study was to provide insight on how to improve agricultural water management to increase agricultural yield while maintaining factory production in the Xinavane sugarcane estate. This was done by analysing the spatiotemporal variability in water productivity, land productivity, and other irrigation performance indicators for different irrigation methods. In addition, the impacts of increasing water productivity to a target value ("closing the productivity gap") is assessed.
Learn more about this case study in our 2-pager or read the full report.
Case Study: Cropmon, Kenya
The objective of this case study is threefold: first, to shed light onto the agronomic factors that affect yield production and water productivity using AquaCrop simulations; second, to facilitate a comparison between the results of AquaCrop and those of WaPOR; and third, to investigate possible causes of discrepancies between WaPOR and AquaCrop results. This was done by conducting AquaCrop simulations and WaPOR analyses on 5 farms in Kenya and comparing the results. Water productivity related indicators (including yield, biomass production, and water consumption, among others) were obtained.
Learn more about this case study in our 2-pager (coming soon) or read the full report.
Case Study: Wonji, Ethiopia
The main objective of this study was to provide insight into water and land productivity, and irrigation performance at the Wonji-Show Sugar Plantation using WaPOR derived data. This was done by analysing the spatial variation among irrigation application methods. Furthermore, the productivity gaps and implications of closing these gaps for production and water use are explored, considering water allocation in Awash River Basin.
Learn more about this case study in our 2-pager or read the full report.