Model card: seed density recommendation model

What it is

Overview

Following the selection of an appropriate seed product (crop variety or hybrid), determining optimal planting density is critical for maximizing crop yield and profitability. The Seed Density Recommendation Model addresses this challenge by providing data-driven insights to optimize seed planting density for Syngenta seed products.

The model integrates multiple factors including crop variety, expected yield, field location, and economic parameters to deliver plant density recommendations at both field and field-zone levels. Through variety-specific and location-tailored recommendations, the model enables users to develop optimized planting strategies for enhanced agricultural outcomes.

Inputs and Outputs

Required inputs

Field and Cropping Information:

• Field location coordinates
• Crop type specification
• Crop variety name
• Expected yield per field or field zone

Note: Recommendations are available exclusively for Syngenta products. The complete product catalog (list of valid product names) is accessible via a separate API endpoint.

Optional inputs

Users may provide additional parameters for enhanced model customization. Default values will be applied when optional inputs are not specified.

Advanced crop practice inputs:

• Planting date: Used for certain region-specific rules and density recommendation limits. Regional variations in planting seasons (e.g., extended planting windows in Argentina and Brazil versus singular planting periods in Europe) influence model recommendations.
• Germination rate: Enables adjustment of recommended seed rates to achieve target plant density post-germination.

Model configuration:

• Seed pricing parameters: Includes seed price, commodity price, and seed bag size. Those parameters are used to calculate the economic optimal density. If not provided, the API won't return the optimal economic plant density.
• Farmer benchmark density: Reference density representing standard farming practices, used for measuring the economic difference between farmer usual practice and model-recommendation.
• Planting density thresholds: User-defined minimum and maximum density limits based on local practices or operational constraints.

Response configuration:

• Output filtering: Enables users to specify required model outputs for customized response formatting.

Outputs

• Density response curves: Predicted yield values across varying planting densities, enabling visualization of yield-density relationships and comparison between optimal biological and economic plant densities.
• Optimal biological seed rate and plant density: Target plant density and seed rate recommendations for yield maximization based on field environment and crop practices. Seed rates incorporate user-specified germination rates.
• Optimal economic seed rate and plant density: Target plant population and seed rate recommendations for profit maximization considering seed investment costs and germination rates.
• Comparative analysis: Target plant population and seed rate recommendations for profit maximization considering seed investment costs and germination rates.

How it works

Algorithm principles

The Seed Density Recommendation Model employs an optimization algorithm designed for Syngenta seed products across multiple field crops, regions, and countries. The model synthesizes yield expectations, crop characteristics, and economic factors to generate data-driven planting density recommendations.

The model's core strength lies in its adaptive response system, which calibrates recommendations based on local growing conditions and yield potential. Two complementary optimization approaches are employed:

• Biological Optimization: Maximizes yield potential under given environmental conditions
• Economic Optimization: Maximizes profitability considering seed costs, commodity prices, and environmental factors

The model utilizes a mechanistic-empirical approach integrating established agronomic principles. While the core algorithms are proprietary developments, the underlying scientific framework is based on well-documented yield-density relationships from literature (Tollenaar et al., 1992; Andrade et al., 1999, 2002; Vega et al., 2001).

The mathematical framework incorporates key agronomic equations that describe:

• The non-linear relationship between plant density and yield
• Environmental index effects on optimal plant density (DiMauro et al., 2025)
• Genotype x density interactions

The model was trained and validated using a comprehensive dataset from Syngenta field trials across diverse environments. Each experimental site evaluated multiple Syngenta products under varying plant density treatments ranging from 3 to 15 plants per square meter (pl. m⁻²).

Continuous improvement

• Annual model calibration: Model parameters are recalibrated annually using new field trial data and validated by regional agronomic experts to maintain accuracy and relevance.
• Expanding variety catalogue: New Syngenta seed products are integrated annually through systematic trial data incorporation and expert validation to support new product launches.
• Performance monitoring: Continuous monitoring protocols detect data quality issues and model performance drift through systematic evaluation of output ranges, extreme density predictions, and input parameter validation.

How to interpret insights

Explanation of predicted values

The seed density model provides two key recommendations: Optimal Biological Plant Density and Optimal Economic Plant Density, along with their corresponding yield predictions. These values are calculated based on the expected yield potential of your field.

The density values are provided as plants per square meter (pl/m²), with corresponding seed rates that account for germination rates. Each recommendation includes:

• Target plant density
• Expected yield at that density
• Economic return calculation

Detailed use-case example

Intended use

• Planting strategy optimization: Determination of economically optimal planting densities for specific field conditions and crop varieties/hybrids.
• Economic impact assessment: Enables comparison of different seeding rates and their economic impact, considering seed costs and expected crop prices.
• Field Planning: Helps farmers make informed decisions about seed procurement.

Limitations

User inputs impact

Model performance may be influenced by:

• Precision of user-provided yield expectation estimates
• Accuracy of input cost parameters and commodity price projections

Model limitations

• The model does not independently assess local environmental conditions or zone-specific yield expectations. This site-specific information must be provided as input to ensure accurate density recommendations.

• During the growing season, extreme weather events may significantly impact expected yield per field or field zone. Consequently, the optimal planting densities planned at season start may no longer be appropriate.

The model recommendations should always be used as decision support tools alongside local agronomic expertise and specific field knowledge.