Reconciling yield gains in agronomic trials with returns under African smallholder conditions
Rachid Laajaj
(1)
,
Karen Macours
(2, 3)
,
Cargele Masso
(4)
,
Moses Thuita
(4)
,
Bernard Vanlauwe
(4)
Karen Macours
- Fonction : Auteur
- PersonId : 1286873
- IdHAL : macours-karen
- ORCID : 0000-0002-1585-4418
Résumé
Increased adoption of improved agricultural technologies is considered an essential step to address global poverty and hunger, and agronomic trials suggest intensification in developing countries could result in large yield gains. Yet the promise of new technologies does not always carry over from trials to real-life conditions, and diffusion of many technologies remains limited. We show how parcel and farmer selection, together with behavioural responses in agronomic trials, can explain why yield gain estimates from trials may differ from the yield gains of smallholders using the same inputs under real-life conditions. We provide quantitative evidence by exploiting variation in farmer selection and detailed data collection from research trials in Western Kenya on which large yield increments were observed from improved input packages for maize and soybean. After adjusting for selection, behavioural responses, and other corrections, estimates of yield gains fall to being not significantly different from zero for the input package tested on one of the crops (soybean), but remain high for the other (maize). These results suggest that testing new agricultural technologies in real-world conditions and without researcher interference early in the agricultural research and development process might help with identifying which innovations are more likely to be taken up at scale.
Domaines
Economies et financesFormat du dépôt | Notice |
---|---|
Type de dépôt | Article dans une revue |
Titre |
en
Reconciling yield gains in agronomic trials with returns under African smallholder conditions
|
Résumé |
en
Increased adoption of improved agricultural technologies is considered an essential step to address global poverty and hunger, and agronomic trials suggest intensification in developing countries could result in large yield gains. Yet the promise of new technologies does not always carry over from trials to real-life conditions, and diffusion of many technologies remains limited. We show how parcel and farmer selection, together with behavioural responses in agronomic trials, can explain why yield gain estimates from trials may differ from the yield gains of smallholders using the same inputs under real-life conditions. We provide quantitative evidence by exploiting variation in farmer selection and detailed data collection from research trials in Western Kenya on which large yield increments were observed from improved input packages for maize and soybean. After adjusting for selection, behavioural responses, and other corrections, estimates of yield gains fall to being not significantly different from zero for the input package tested on one of the crops (soybean), but remain high for the other (maize). These results suggest that testing new agricultural technologies in real-world conditions and without researcher interference early in the agricultural research and development process might help with identifying which innovations are more likely to be taken up at scale.
|
Auteur(s) |
Rachid Laajaj
1
, Karen Macours
2, 3
, Cargele Masso
4
, Moses Thuita
4
, Bernard Vanlauwe
4
1
UNIANDES -
Universidad de los Andes [Bogota]
( 229853 )
- Bogotá - Colombia
- Colombie
2
PSE -
Paris School of Economics
( 301309 )
- 48 boulevard Jourdan 75014 Paris
- France
3
PJSE -
Paris Jourdan Sciences Economiques
( 578027 )
- 48 boulevard Jourdan 75014 Paris
- France
4
IITA-DRC -
International Institute of Tropical Agriculture
( 444410 )
- BP 4163
Kinshasa
- Congo-Kinshasa
|
Langue du document |
Anglais
|
Nom de la revue |
|
Vulgarisation |
Non
|
Comité de lecture |
Oui
|
Audience |
Internationale
|
Date de publication |
2020-12
|
Public visé |
Scientifique
|
Volume |
10
|
Numéro |
1
|
Numéro d'article |
|
Domaine(s) |
|
Projet(s) ANR |
|
DOI | 10.1038/s41598-020-71155-y |
Pubmed Id | 32868856 |
PubMed Central | PMC7459313 |
UT key WOS | 000570035500033 |
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