Egesi, Chiedozie N.

Ikeogu, Ugochukwu N.

Jannink, Jean-Luc

Tufan, Hale Ann

CGSpace

1000	minds- Economic	Weights	for	Gendered	Traits?Poster	authors:	Hale	Ann	Tufan,	Ugochukwu Ikeogu,	Jean-Luc	Jannink,	Chiedozie EgesiContact:	Hale	Ann	Tufan (hat36@cornell.edu)Relevance,	where	it	fits	in	breeding	cycleDescription	of	steps	or	stagesNext	steps	in	developing	tool/approachExample	of	usePartners1000	minds	(https://www.1000minds.com/) is	a	decision	making	software	with	a	wide	range	of	uses,	including	plant	and	animal	breeding.	It	has	been	used	in	cattle	and	sheep	breeding,	forage	grasses	and	most	recently	on	cassava.	The	novel	use	of	a	pair	wise	ranking	method	called	PAPRIKA	(Hansen	and	Ombler,	2008),	enables	users	to	administer	surveys	to	determine	“weights”	of	each	attribute	of	a	product.	The	software	has	been	used	to	set	breeding	priorities	for	a	diversity	of	uses:	setting	economic	values	for	traits	in	sheep	breeding	(Byrne	et	al.	2012),	forages	(Smith	and	Fenessy,	2014),	as	well	as	developing	dairy	farmer	typologies	(Martin-Collado et	al.,	2015	and	Slagbloom et	al.,	2016)	and	setting	economic	values	for	traits	in	the	Australian	dairy	farming	industry	(Byrne	et	al.,	2016).	We	are	exploring	the	use	of	1000	minds	to	develop	economic	weights	for	cassava	breeding	in	Uganda,	Tanzania	and	Nigeria,	specifically	to	meet	gender	responsive	and	user	oriented	breeding	targets.https://www.1000minds.com/The	1000	minds	choice	experimentation	survey	design	begins	by	defining	the	attributes,	concepts	and	choices	that	will	be	presented	in	the	survey.	For	breeding,	attributes	can	be	”traits”	(yield,	disease,	protein/starch	content	etc)	along	with	levels	that	are	defined	for	each	(kg	yield,	amount	of	disease,	amount	of	protein/starch	per	animal/plant	etc).	Adapting	the	1000	minds	method	to	gender	responsive	breeding	will	require	detailed		development	work	to	define	traits	and	ranges,	as	well	as	refine	the	interface	for	use	in	the	field	setting	directly	with	cassava	farmers,	processors	and	consumers.	Points	for	World	Café	discussion:1. How	can	the	software	be	used	“offline”	and	still	give	users	an	opportunity	for	direct	input?2. How	can	we	define	traits	and	ranges	in	a	standardized	manner- need	for	pre-survey	focus	groups?3. What	are	considerations	we	should	have	in	beta-testing	to	ensure	the	tool	is	user-friendly	for	example	for	women,	young	and	illiterate	respondents?4. Can	we	adapt	the	tool	to	be	more	“gender	responsive”	beyond	recording	age,	sex	etc of	respondent- what	do	we	need	to	think	about?Example	of	building	“attributes”	from	traits	of	interest	in	cassava.	Three	traits	were	chosen	for	this	study:	Fresh	yield	(tons/hectare),	dry	matter	content	(%)	and	cassava	mosaic	disease	(rank	1-5).	For	each,	three	levels	of	possibilities	were	defined,	based	on	breeder	criteria.Source:	Ugochukwu IkeoguThe	PAPRIKA	method	then	involves	respondents	replying	to	a	series	of	questions,	each	comparing	two	hypothetical	concepts,	and	involving	a	trade-off.	This	is	repeated	up	to	30-40	times	depending	on	the	number	of	attributes,	with	each	round	of	questions	being	an	adaptation	based	on	responses	to	the	previous,	a	type	of	adaptive	conjoint	analysis.	The	software	then	calculates	‘part-worth	utilities’,	or	relative	weights,	for	each	attribute.	If	pre-set	concepts	(combinations	for	attributes)	were	defined,	the	results	also	rank	concepts	for	their	total	’value’.In	a	1000	minds	design	experiment,	Ugochukwu Ikeogu tested	the	decision	making	tool	for	three	cassava	traits	at	three	levels	each.	This	test	revealed	the	need	for	better	understanding	and	definition	of	traits	before	developing	the	survey.	Without	clear	definition	and	reference	ranges	for	“good	gari”,	“big	roots”	and	“matures	early”,	emerging	as	preferred	cassava	traits	from	open	ended	interviews	and	FGDs,	it	will	be	nearly	impossible	to	use	these	for	1000	minds.	Further	work	will	need	to	be	carried	out	to	refine	the	tool	before	deploying	with	usersSource:	Ugochukwu Ikeogu

1000 minds- Economic Weights for Gendered Traits?

https://cgspace.cgiar.org/bitstream/10568/89297/1/2.3%20Hale%20Ann%20Tufan_1000%20Minds.pdf

1000 minds- Economic Weights for Gendered Traits?

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