Optimizing crop clustering to minimize pathogen invasion in agriculture

Area of Science:

• Agricultural Science
• Epidemiology
• Mathematical Modeling

Background:

• Pathogen invasion in crops depends on spatial arrangement of susceptible plants and pathogen dispersal.
• Previous research indicates crop clustering reduces invasion rates, but optimal configurations remain unidentified.

Purpose of the Study:

• To analytically determine the optimal range of crop clustering that minimizes initial pathogen invasion rates.
• To provide insights for optimizing agricultural landscape structure to mitigate pathogen spread.

Main Methods:

• Developed an analytical approximation for pathogen invasion rate in artificial square-clustered agricultural landscapes.
• Utilized Gaussian, negative exponential, and power-law dispersal kernels to model pathogen movement.
• Validated the analytical model against computer simulations and analyzed a real-world cassava landscape.

Main Results:

• Identified specific ranges of cluster size and separation distance that minimize initial pathogen invasion rates.
• The analytical approximation showed strong agreement with simulation results across different dispersal kernels.
• Demonstrated application to a cassava landscape, providing strategies to mitigate cassava brown streak virus.

Conclusions:

• Optimal crop clustering strategies can be analytically derived to effectively reduce initial pathogen invasion rates.
• These findings offer valuable guidance for farmers and agricultural planners in designing resilient landscapes.
• The methodology is adaptable to various landscape geometries and pathogen dispersal characteristics.