LAUSR

Abstract Crop-growth models are powerful tools for supporting optimal planning and management of agricultural water use globally. However, use of crop models for this purpose often requires advanced programming expertize and computational resources, limiting the potential uptake in integrated water management research by practitioners such as water managers, policymakers, and irrigation service providers. In this article, we present AquaCrop-OSPy (ACOSP), an open source, Python implementation of the crop-water productivity model AquaCrop. The model provides a user friendly, flexible and computationally efficient solution to support agricultural water management, which can be readily integrated with other Python modules or code bases and run instantly via a web browser using the cloud computing platform Google Colab without the need for local installation. This article describes how to run basic simulations using AquaCrop-OSPy, along with more advanced analyses such as optimizing irrigation schedules and evaluating climate change impacts. Each use case is paired with a Jupyter Notebook, which offer an interactive learning environment for users and can be readily adapted to address a range of common irrigation planning and management challenges faced by researcher, policymakers and businesses in both developed and developing countries (https://github.com/thomasdkelly/aquacrop).