Crop model determined mega-environments for cassava yield trials on paddy fields following rice

Area of Science:

• Agricultural Science
• Crop Modeling
• Agronomy

Background:

• Cassava (Manihot esculenta) is a vital crop, but optimizing its cultivation in diverse environments like Thailand's paddy fields requires strategic planning.
• Identifying suitable mega-environments is crucial for efficient cassava yield trials and resource allocation.

Purpose of the Study:

• To assess the applicability of the Cropping System Model (CSM)-MANIHOT-Cassava for grouping paddy field environments in Northeast Thailand into mega-environments.
• To determine if cassava production in these specific conditions can be effectively categorized for targeted research.

Main Methods:

• Simulated growth and yield data for four cassava genotypes across various soil types and weather conditions (1988-2017) on paddy fields post-rice harvest.
• Employed the genotype main effect plus genotype by environment interaction (GGE) biplot technique for mega-environment grouping.
• Utilized average simulated storage root dry weight (SDW) over 30 years for robust analysis.

Main Results:

• Yearly analyses showed inconsistencies in environment grouping and identifying top-performing genotypes.
• A 30-year average analysis using GGE biplot successfully grouped all 41 simulated environments into two distinct mega-environments.
• The CSM-MANIHOT-Cassava model demonstrated effectiveness in identifying mega-environments for off-season rice paddy cultivation.

Conclusions:

• The CSM-MANIHOT-Cassava model is a valuable tool for identifying mega-environments for cassava yield trials in rice-based systems.
• This approach can significantly reduce the time and resources required for optimizing cassava cultivation research in Thailand's paddy fields.