MASSAI: Multi-agent system for simulating sustainable agricultural intensification of smallholder farms in Africa

Powell Mponela; Bao Le; Sieglinde Snapp; Grace B Villamor; Lulseged Tamene; Christian Borgemeister

doi:10.1016/j.mex.2023.102467

MASSAI: Multi-agent system for simulating sustainable agricultural intensification of smallholder farms in Africa

MethodsX. 2023 Oct 30:11:102467. doi: 10.1016/j.mex.2023.102467. eCollection 2023 Dec.

Authors

Powell Mponela^{1

2}, Bao Le³, Sieglinde Snapp⁴, Grace B Villamor^{1

5}, Lulseged Tamene², Christian Borgemeister¹

Affiliations

¹ Center for Development Research, University of Bonn, Genscherallee 3, Bonn 53113, Germany.
² International Center for Tropical Agriculture (CIAT), Gurd Sholla Area, Bole Sub-city, Woreda 6, Addis ababa P.O. Box 5689, Ethiopia.
³ CGIAR Research Program on Grain Legumes and Dry Cereals (CRP GLDC), International Center for Agricultural Research in the Dry Areas (ICARDA), 2 Port Said, Victoria Sq., Ismail El-Shaaer Building, Maadi, Cairo, Egypt.
⁴ Department of Plant, Soil and Microbial Sciences, Michigan State University, 1066 Bogue St., East Lansing, MI 48824, USA.
⁵ New Zealand Forest Research Institute, Ltd (Scion), Te Papa Tipu Innovation Park, 49 Sala Street, Rotorua, 3046, New Zealand.

Abstract

The research and development needed to achieve sustainability of African smallholder agricultural and natural systems has led to a wide array of theoretical frameworks for conceptualising socioecological processes and functions. However, there are few analytical tools for spatio-temporal empirical approaches to implement use cases, which is a prerequisite to understand the performance of smallholder farms in the real world. This study builds a multi-agent system (MAS) to operationalise the Sustainable Agricultural Intensification (SAI) theoretical framework (MASSAI). This is an essential tool for spatio-temporal simulation of farm productivity to evaluate sustainability trends into the future at fine scale of a managed plot. MASSAI evaluates dynamic nutrient transfer using smallholder nutrient monitoring functions which have been calibrated with parameters from Malawi and the region. It integrates two modules: the Environmental (EM) and Behavioural (BM) ones.•The EM assess dynamic natural nutrient inputs (sedimentation and atmospheric deposition) and outputs (leaching, erosion and gaseous loses) as a product of bioclimatic factors and land use activities.•An integrated BM assess the impact of farmer decisions which influence farm-level inputs (fertilizer, manure, biological N fixation) and outputs (crop yields and associated grain).•A use case of input subsidies, common in Africa, markedly influence fertilizer access and the impact of different policy scenarios on decision-making, crop productivity, and nutrient balance are simulated. This is of use for empirical analysis smallholder's sustainability trajectories given the pro-poor development policy support.

Keywords: Farm productivity; Farmer behaviour; MASSAI: Multi-Agent System for Simulating Agricultural Intensification; Nutrient balance; Re-orienting farm input subsidy.