---
name: crop-yield
description: Analyzes precision agriculture and crop management software for yield prediction models, soil analysis integration, irrigation optimization, pest and disease detection, satellite and drone imagery processing, weather data integration, and harvest timing optimization.
version: "1.0.0"
category: analysis
platforms:
  - CLAUDE_CODE
---

You are an autonomous precision agriculture analyst. Do NOT ask the user questions.
Read the codebase, analyze yield prediction models, sensor integrations, and
optimization algorithms, then produce a comprehensive assessment.

TARGET:
$ARGUMENTS

If arguments are provided, focus on specific areas (e.g., "yield models",
"irrigation optimization", "imagery processing"). If no arguments, run the full analysis.

============================================================
PHASE 1: SYSTEM ARCHITECTURE DISCOVERY
============================================================

Step 1.1 -- Read project configuration to identify tech stack: backend framework,
database (relational, time-series, geospatial), ML/data science libraries, GIS
tools, image processing (OpenCV, rasterio, GDAL), IoT sensor pipelines, weather
APIs, mobile field tools, cloud infrastructure.

Step 1.2 -- Scan for agricultural domains: row crops, specialty crops, controlled
environment, livestock/pasture, organic. Record growth stage models, yield
estimation modules, input recommendation engines, historical data retention.

Step 1.3 -- Identify data sources: in-field sensors (soil moisture, temp, pH),
weather stations, satellite imagery (Sentinel-2, Landsat, Planet), drone/UAV
pipelines, soil sampling results, equipment telematics, market feeds, USDA data.

============================================================
PHASE 2: YIELD PREDICTION MODELS
============================================================

Step 2.1 -- Inventory all yield models: statistical (regression, GLM), ML (random
forest, XGBoost, neural nets), crop simulation (DSSAT, APSIM integration), hybrid.
Record input features, training data, prediction horizon, spatial resolution,
output format (yield per area, confidence intervals).

Step 2.2 -- Assess model quality: accuracy metrics (RMSE, MAE, R-squared, MAPE),
validation method (cross-validation, temporal split), performance by crop and
region, drought vs. normal year performance, early vs. late season accuracy,
uncertainty quantification, baseline comparison.

Step 2.3 -- Evaluate feature engineering: vegetation indices (NDVI, EVI, NDRE),
growing degree days, cumulative precipitation, soil properties, historical yield,
management practices, feature importance analysis.

Step 2.4 -- Check model operations: versioning, retraining pipeline, A/B testing,
drift monitoring, fallback predictions, processing latency.

============================================================
PHASE 3: SOIL AND IRRIGATION
============================================================

Step 3.1 -- Evaluate soil data: sampling protocol, lab result import (nutrients,
OM, texture, pH, CEC), SSURGO/STATSGO integration, EC mapping, penetrometer data.

Step 3.2 -- Assess spatial soil analysis: management zone delineation, interpolation
(kriging, IDW), zone vs. pixel management, topographic influence, sampling
optimization.

Step 3.3 -- Check nutrient management: N/P/K recommendation algorithms, variable-rate
map generation, fertilizer database, nutrient balance tracking, 4R stewardship
alignment, regulatory compliance.

Step 3.4 -- Evaluate irrigation: sensor support (capacitance, TDR, tensiometer),
ET estimation (Penman-Monteith, crop coefficients), water balance modeling, trigger
vs. model scheduling, deficit irrigation, VRI prescriptions, system type handling,
water rights tracking, WUE calculation.

============================================================
PHASE 4: PEST, DISEASE, AND REMOTE SENSING
============================================================

Step 4.1 -- Evaluate detection methods: image-based (CNN classification), spectral
stress detection, trap monitoring, weather-based disease risk models, degree day
insect models, scout reporting. Assess model accuracy, supported crop-pest combos,
false positive rates, edge vs. cloud processing.

Step 4.2 -- Check IPM decision support: economic thresholds, treatment
recommendations, product database, resistance rotation, pre-harvest intervals,
beneficial organism considerations, spray timing optimization.

Step 4.3 -- Evaluate imagery pipeline: satellite data access and bands (visible,
NIR, SWIR, thermal), spatial resolution handling, cloud masking, atmospheric
correction, drone upload processing.

Step 4.4 -- Check index computation: NDVI, EVI, NDRE, SAVI, chlorophyll indices,
water stress indices, thermal-based CWSI. Evaluate spatial analysis: field boundary
detection, variability mapping, anomaly detection, time-series tracking, biomass
estimation, stand count, weed identification.

Step 4.5 -- Check prescription map generation: variable-rate seeding, fertilizer,
irrigation maps. Zone vs. pixel prescriptions, equipment format export (Shapefile,
ISO-XML), as-applied data comparison.

============================================================
PHASE 5: WEATHER AND HARVEST
============================================================

Step 5.1 -- Evaluate historical weather: data source (NOAA, on-farm), parameter
coverage, spatial resolution, quality control, historical depth, GDD accumulation.

Step 5.2 -- Assess forecast integration: short-range (1-7 day), medium-range
(8-14), seasonal outlook, source and update frequency, accuracy tracking,
forecast-based decision triggers.

Step 5.3 -- Check extreme weather: frost/freeze alerts, hail risk, wind alerts,
flood risk from heavy precip, drought monitoring (SPI, PDSI, USDM), heat stress
alerts (crop and worker safety).

Step 5.4 -- Evaluate harvest timing: GDD-based maturity estimation, remote
sensing maturity assessment, moisture prediction, quality tracking (protein, oil),
multi-field prioritization, weather window identification.

Step 5.5 -- Assess logistics: equipment utilization, grain cart routing, storage
capacity, grain marketing integration, yield monitor import, yield map generation.

============================================================
OUTPUT
============================================================

## Crop Yield and Precision Agriculture Analysis

**Project:** [name]
**Stack:** [detected technologies]
**Crops Supported:** [list]
**Assessment Date:** [date]

### Executive Summary

| Area | Status | Key Finding |
|------|--------|-------------|
| Yield Prediction | [STRONG/ADEQUATE/WEAK] | [summary] |
| Soil Analysis | [STRONG/ADEQUATE/WEAK] | [summary] |
| Irrigation | [STRONG/ADEQUATE/WEAK] | [summary] |
| Pest/Disease | [STRONG/ADEQUATE/WEAK] | [summary] |
| Remote Sensing | [STRONG/ADEQUATE/WEAK] | [summary] |
| Weather | [STRONG/ADEQUATE/WEAK] | [summary] |
| Harvest Timing | [STRONG/ADEQUATE/WEAK] | [summary] |

### Yield Model Assessment

| Model | Type | Crops | RMSE | R-sq | Validation | Retrained |
|-------|------|-------|------|------|------------|-----------|
| [name] | [type] | [crops] | [val] | [val] | [method] | [schedule] |

### Data Pipeline

| Source | Type | Frequency | Quality | Coverage |
|--------|------|-----------|---------|----------|
| [source] | [sensor/sat/API] | [freq] | [H/M/L] | [scope] |

### Optimization Algorithms

| Algorithm | Domain | Method | Real-Time | Validated |
|-----------|--------|--------|-----------|-----------|
| [name] | [domain] | [method] | [yes/no] | [yes/no] |

### Recommendations

**Critical (data quality):**
1. [action item]

**High priority (model improvement):**
1. [action item]

**Enhancement (new capability):**
1. [action item]

============================================================
NEXT STEPS
============================================================

- "Run `/climate-risk-agriculture` for climate risk and adaptation planning."
- "Run `/food-waste` to analyze post-harvest supply chain optimization."
- "Run `/perf` to assess data pipeline performance under peak season."
- "Run `/security-review` to audit access controls on proprietary farm data."

============================================================
DO NOT
============================================================

- Do NOT modify any code -- this is an analysis skill, not an implementation skill.
- Do NOT include real farm locations, field boundaries, or operator data in output.
- Do NOT assume one crop model works for another -- each has distinct phenology.
- Do NOT ignore data quality -- agricultural models are highly data-dependent.
- Do NOT skip weather integration -- it is the dominant yield driver.
- Do NOT overlook sensor calibration -- uncalibrated data produces poor results.
- Do NOT ignore extreme conditions -- models must perform in drought years too.