000			04227nab|a22004817a|4500
001			69559
003			MX-TxCIM
005			20251218165823.0
008			251120s2025 ne ||||| |||| 00| 0 eng d
024	8		_ahttps://dx.doi.org/10.2139/ssrn.5704423
040			_aMX-TxCIM
041			_aeng
100	1		_8001713480 _aChiduwa, M.S. _gSustainable Agrifood Systems _929879
245	1	4	_aAgronomic levers to increase maize and soybean productivity across the Chinyanja Triangle, Southern Africa
260			_aNetherlands : _bElsevier, _c2025.
500			_aPre-print
500			_aOpen Access
520			_aCONTEXTMaize is Southern Africa’s staple food crop, while soybean, a multi-purpose legume, is the fastest expanding crop in area and production in the region. Despite their importance, yields remain low, highlighting the need for context-specific strategies to sustainably increase productivity.OBJECTIVEThis study characterized maize and soybean production systems across the Chinyanja Triangle, estimated yield gaps, and identified agronomic levers for yield improvement.METHODSYields were measured using crop-cuts in farmers’ fields in Kasungu and Lilongwe (Malawi), Sinda and Katete (Zambia) and Angonia (Mozambique), alongside a diagnostic survey on crop management practices during the 2022-2023 season. A total of 485 maize and 509 soybean field observations were analyzed, supplemented with secondary climate and soil data, and water-limited yields simulated with the DSSAT crop model. A machine learning approach combining random forest and Shapley values was used to explain yield variability and identify yield constraints.RESULTS AND CONCLUSIONSActual maize yields across districts ranged between 2.2 and 2.6 t ha-1 on average and actual soybean yields between 0.4 and 1.6 t ha-1. Simulated water-limited yields were greater than 8.0 t ha-1 for maize and than 3.5 t ha-1 for soybean. Maize cropping systems were similar across districts, whereas an intensification pathway was found for soybean cropping systems in Malawi, an extensification pathway in Zambia and marginal production pathway in Mozambique. Yield constraints for maize included low plant population and fertilizer management and variety type, while soybean yield constraints hinged around soil fertility, sowing date and variety type.SIGNIFICANCEThe agronomic levers identified can be used to target technology development and prioritization of interventions to increase productivity sustainable in the region. These insights support strategic planning for sustainable intensification and food security across Southern Africa.
546			_aText in English
597			_bExcellence in Agronomy _bMixed Farming Systems _dCGIAR Trust Fund _aClimate adaptation & mitigation _aEnvironmental health & biodiversity _aNutrition, health & food security _aPoverty reduction, livelihoods & jobs _cSystems Transformation _cResilient Agrifood Systems _uhttps://hdl.handle.net/10568/178721
650		7	_aCrop management _2AGROVOC _91061
650		7	_aFood security _2AGROVOC _91118
650		7	_aSustainable intensification _2AGROVOC _91355
650		7	_aYield gap _2AGROVOC _91356
651		7	_aSouthern Africa _2AGROVOC _91954
700	1		_aNyagumbo, I. _gSustainable Agrifood Systems _8INT3097 _9891
700	1		_aOmondi, J.O. _918775
700	1		_aMkuhlani, S. _91809
700	1		_aMasikati, P. _9198
700	1		_aMalunga, I.M. _8001713866 _gFormerly Sustainable Agrifood Systems _938115
700	0		_aMulundu Mwila _934202
700	1		_aSimwaka, P. _910902
700	1		_aMapaure, M. _938178
700	1		_aBanda, A. _940587
700	0		_aDeo-Gratias Judrita Mawugnon Hougni _8001714252 _gSustainable Agrifood Systems _937564
700	1		_8001713635 _aNdour, A. _gSustainable Agrifood Systems _929881
700	1		_aFantaye, K.T. _gSustainable Agrifood Systems _8INT3458 _9956
700	1		_aSnapp, S.S. _8001712907 _gSustainable Agrifood Systems _97149
700	1		_aSilva, J.V. _8001712458 _gSustainable Agrifood Systems _99320
773	0		_tSocial Science Research Network (SSRN) _gIn press _dNetherlands : Elsevier, 2025.
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/36130
942			_cJA _n0 _2ddc
999			_c69559 _d69551