Filling the agronomic data gap through a minimum data collection approach.

Area of Science:

• Agricultural Science
• Agronomy
• Data Collection Methodologies

Background:

• Agronomic data (inputs, management, yields) are crucial for assessing cropping system performance but are often unavailable or aggregated.
• Existing data limitations hinder comprehensive analysis of productivity, nutrient balances, and resource use efficiency.
• There is a need for efficient methods to collect site-specific agronomic data for major crop-producing areas.

Purpose of the Study:

• To develop and validate a stratified sampling approach for identifying optimal sites for agronomic data collection.
• To balance data collection efficiency with the accurate representation of major crop-producing regions.
• To address data gaps in agronomic information for key crop systems.

Main Methods:

• A stratified sampling method was employed, using a spatial framework of climate zones and crop distribution maps.
• Sampling areas (SA) were selected to cover at least 50% of the national harvested area for a given crop.
• The approach was validated using fertilizer application data for maize, wheat, and soybean in Argentina, USA, and Kenya.

Main Results:

• The approach identified 10-28 sampling areas per country (Argentina, USA, Kenya).
• Data from a small number of experts or fields within selected SAs provided robust estimates of average fertilizer rates.
• National-level fertilizer rates derived using this method closely matched those from complete datasets, requiring less than a third of the observations.

Conclusions:

• The developed minimum crop data collection approach effectively fills agronomic data gaps in a cost-effective manner.
• This method is suitable for major crop systems, encompassing both large- and small-scale farming operations.
• The approach is generic and applicable to any crop-country combination, requiring modest investment for data collection.