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

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Date

2023-11-01

Date Issued

2023-11-01

Contributes to SDGs

SDG 1 - No povertySDG 2 - Zero hungerSDG 3 - Good health and well-beingSDG 12 - Responsible consumption and productionSDG 15 - Life on land

ISI Journal

Impact factor: 1.9 (Year: 2023)

Authors

Citation

Mponela, P. Le, Q. B. Snapp, S. Villamor, G. B. Tamene, L. Borgemeister, C. 2023. MASSAI: Multi-agent system for simulating sustainable agricultural intensification of smallholder farms in Africa. MethodsX 11, 102467.

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.

Permanent link

https://hdl.handle.net/20.500.11766/68794

DOI

https://dx.doi.org/10.1016/j.mex.2023.102467

Collections

Agricultural Research Knowledge

Other URI

https://github.com/powellmponel/MASSAI

AGROVOC Keyword(s)

nutrient balance ; maize

Subject(s)

farm productivity; re-orienting farm input subsidy; farmer behaviour

Author(s) ORCID(s)

Mponela, Powell  https://orcid.org/0000-0003-4269-0663
 Le, Quang Bao  https://orcid.org/0000-0001-8514-1088
 Tamene, Lulseged  https://orcid.org/0000-0002-3806-8890