000			03080nam a2200349 4500
001			64912
003			MX-TxCIM
005			20230131220407.0
008			201218s2021 mx ||||| |||| 00| 0 eng d
040			_aMX-TxCIM
041			_aeng
100	1		_aMottaleb, K.A. _gFormerly Socioeconomics Program _gFormerly Sustainable Agrifood Systems _8I1706152 _9810
245	1	0	_aWheat consumption dynamics in selected countries in Asia and Africa : _bimplications for wheat supply by 2030 and 2050
260			_aEl Batan, Texcoco (Mexico) : _bCIMMYT, _c2021.
300			_avi, 24 pages
490			_aIntegrated Development Program Discussion Paper ; _vno. 002
500			_aOpen Access
520			_aThe emerging 4th industrial revolution is having a profound effect on the direction of agrarian development. Big data technologies are becoming embedded within all walks of life, leading to both significant advancements in utility and to critical ethical concerns about the organization of the social world. Academic attention is growing into how such technologies can be employed for farmers; using enriched forms of data collection to account for contextually embedded factors in smallholder decision making. Further, in the context of ongoing COVID-19 restrictions, research is increasingly being conducted remotely. This removes a significant interpersonal dimension from studies, a particular concern for those which deal with sensitive data such as gender empowerment. In this paper we explore emotion classification and sentiment analysis of text and audio data of farmers' interviews in eastern and southern Africa and their evaluation of a set of sustainable agricultural practices. With this relatively benign dataset, which is known not to include any instances of affective behavior beyond normal discussion of farming techniques, we attempt to test the viability of these tools and what steps are necessary to make them reliable and accessible to researchers. Findings indicate additional insight can be made to support qualitative study, in several cases demonstrating a convergence between traditional anthropological assessment and expected emotional reaction. There are also unexpected responses and unforeseen learning for the process of qualitative data collection and processing. For future research and interventions, however, a series of limitations and developments are identified for this methodology to mature.
546			_aText in English
650		7	_2AGROVOC _91310 _aWheat
650		7	_2AGROVOC _95504 _aConsumption
650		7	_95501 _aSupply _2AGROVOC
650		7	_2AGROVOC _99096 _aDemand
650		7	_2AGROVOC _98727 _aTime Series Analysis
651		7	_2AGROVOC _91316 _aAfrica
651		7	_2AGROVOC _94026 _aAsia
700	1		_aSonder, K. _gSocioeconomics Program _gSustainable Agrifood Systems _8INT3032 _9882
700	1		_aLopez-Ridaura, S. _gSustainable Intensification Program _gSustainable Agrifood Systems _8INT3360 _9939
700	1		_98304 _aFrija, A.
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21871
942			_cBK _n0 _2ddc
999			_c64912 _d64904