aquacrop-ospy-knowledge

Name: aquacrop-ospy-knowledge
Availability: InStock
Author: lzwei196

Execute AquaCrop-OSPy simulations for crop-water productivity and deficit irrigation optimization.

1|Updated Mar 31, 2026

Search Tags:#automation #workflow #irrigation #aquacrop #deficit_irrigation #water_productivity #crop_model

Ready to run

npx skills add https://github.com/lzwei196/KISS---Knowledge-Infrastructure-for-Scientific-Simulation --skill aquacrop-ospy-knowledge

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aquacrop-ospy-knowledge
Source: https://github.com/lzwei196/KISS---Knowledge-Infrastructure-for-Scientific-Simulation/tree/main/models/AquaCrop
Command: npx skills add https://github.com/lzwei196/KISS---Knowledge-Infrastructure-for-Scientific-Simulation --skill aquacrop-ospy-knowledge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, xarray, matplotlib, aquacrop, ki_tools_common.

What problem does it solve?

AquaCrop-OSPy requires a long sequence of integrated steps to run a simulation, from selecting a crop and configuring soil to preparing weather, setting irrigation and field management, assembling the model, executing, and analyzing outputs. This Skill consolidates the end-to-end workflow into a single, discoverable unit that an AI agent can execute autonomously.

Core Features & Use Cases

End-to-end automation: orchestrates S1–S10 stages from crop selection to water productivity analysis.
Deficit irrigation optimization: supports SMT sweeps and comparison across multiple irrigation strategies.
Reproducible data pipelines: integrates weather, soil, crop, initial conditions, irrigation, and field management with preflight checks and validation.
Use Case: Run a complete AquaCrop workflow for maize at a given location and compare WP across rainfed vs deficit irrigation scenarios.

Quick Start

Run AquaCrop-OSPy end-to-end for a maize scenario using the default pipeline and extract water productivity metrics.

Frequently Asked Questions about aquacrop-ospy-knowledge

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate AquaCrop-OSPy simulations for crop-water productivity analysis?▼

To automate AquaCrop-OSPy simulations, this Skill orchestrates an end-to-end workflow from crop selection and weather preparation to model execution and water productivity analysis, consolidating integrated steps into a single autonomous run.

What is deficit irrigation optimization and how does it work with AquaCrop?▼

Deficit irrigation optimization with AquaCrop involves running SMT sweeps to compare multiple irrigation strategies. This Skill automates these sweeps, allowing rapid evaluation of water productivity across different deficit irrigation scenarios.

Can I use AquaCrop-OSPy to compare rainfed and deficit irrigation scenarios for maize?▼

Yes, you can use AquaCrop-OSPy to compare rainfed and deficit irrigation scenarios for maize. This Skill automates the complete workflow, integrating weather and soil data to extract and compare water productivity metrics across strategies.

Do I need to prepare weather and soil data before running an AquaCrop workflow?▼

You need weather, soil, crop, and field management data to run an AquaCrop workflow. This Skill integrates these inputs with preflight validations and diagnostic checks, ensuring reproducible data pipelines before model assembly and execution.

What are the limitations of automating crop model simulations using Python?▼

Automating crop model simulations in Python depends on specific libraries like numpy, pandas, xarray, and the aquacrop package. Complex custom field management or highly localized weather data integration may require manual preflight adjustments before autonomous execution.