000			03497nab|a22004337a|4500
001			68534
003			MX-TxCIM
005			20250123132617.0
008			250123s2019 ne ||||| |||| 00| 0 eng d
022			_a2215-0161 (Online)
024	8		_ahttps://doi.org/10.1016/j.mex.2023.102467
040			_aMX-TxCIM
041			_aeng
100	1		_aMponela, P. _8001714263 _gSustainable Agrifood Systems _921640
245	1	0	_aMASSAI : _bMulti-agent system for simulating sustainable agricultural intensification of smallholder farms in Africa
260			_aNetherlands : _bElsevier B.V., _c2023.
500			_aPeer review
500			_aOpen Access
520			_aThe 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.
546			_aText in English
591			_aMponela, P. : No CIMMYT Affiliation
591			_aSnapp, S.S. : No CIMMYT Affiliation
597			_aClimate adaptation & mitigation _aNutrition, health & food security _aPoverty reduction, livelihoods & jobs _bMixed Farming Systems _cResilient Agrifood Systems _dCGIAR Trust Fund _dDeutscher Akademischer Austauschdienst (DAAD) _uhttps://hdl.handle.net/10568/132879
650		7	_aFarm Inputs _2AGROVOC _98734
650		7	_aNutrient balance _2AGROVOC _97132
650		7	_aFarming systems _2AGROVOC _91109
650		7	_aModelling _2AGROVOC _911710
650		7	_aPolicies _2AGROVOC _94809
650		7	_aSoil quality _2AGROVOC _91270
650		7	_aSustainability _2AGROVOC _91283
651		7	_aAfrica _2AGROVOC _91316
700	0		_aQuang Bao Le _938017
700	1		_aSnapp, S.S. _8001712907 _gSustainable Agrifood Systems _97149
700	1		_aVillamor, G.B. _921642
700	1		_aTamene, L. _920122
700	1		_aBorgemeister, C. _93762
773	0		_tFrontiers in Ecology and the Environment _gv. 21, no. 7, p. 341-349 _dUnited States of America : Wiley-Blackwell, 2023. _x1540-9295
942			_cJA _n0 _2ddc
999			_c68534 _d68526