Your search found 2 records
1 Schut, M.; van Asten, P.; Okafor, C.; Hicintuka, C.; Mapatano, S.; Nabahungu, N. L.; Kagabo, D.; Muchunguzi, P.; Njukwe, E.; Dontsop-Nguezet, P. M.; Sartas, M.; Vanlauwe, B. 2016. Sustainable intensification of agricultural systems in the Central African Highlands: the need for institutional innovation. Agricultural Systems, 145:165-176. [doi: https://doi.org/10.1016/j.agsy.2016.03.005]
Sustainable agriculture ; Farming systems ; Intensification ; Agricultural research ; Participatory approaches ; Innovation ; Institutional development ; Nongovernmental organizations ; CGIAR ; Stakeholders ; Constraints ; Farmers ; Highlands / Africa South of Sahara / Central Africa / Democratic Republic of the Congo / Rwanda / Burundi
(Location: IWMI HQ Call no: e-copy only Record No: H047848)
http://www.sciencedirect.com/science/article/pii/S0308521X16300440/pdfft?md5=5be37a48e32bcbda5ad290093053ebe8&pid=1-s2.0-S0308521X16300440-main.pdf
https://vlibrary.iwmi.org/pdf/H047848.pdf
https://vlibrary.iwmi.org/pdf/H047848.pdf
(0.81 MB) (828 KB)
This study identifies entry points for innovation for sustainable intensification of agricultural systems. An agricultural innovation systems approach is used to provide a holistic image of (relations between) constraints faced by different stakeholder groups, the dimensions and causes of these constraints, and intervention levels, timeframes and types of innovations needed. Our data shows that constraints for sustainable intensification of agricultural systems are mainly of economic and institutional nature. Constraints are caused by the absence, or poor functioning of institutions such as policies and markets, limited capabilities and financial resources, and ineffective interaction and collaboration between stakeholders. Addressing these constraints would mainly require short- and middle-term productivity and institutional innovations, combined with middle- to long-term NRM innovations across farm and national levels. Institutional innovation (e.g. better access to credit, services, inputs and markets) is required to address 69% of the constraints for sustainable intensification in the Central Africa Highlands. This needs to go hand in hand with productivity innovation (e.g. improved knowhow of agricultural production techniques, and effective use of inputs) and NRM innovation (e.g. targeted nutrient applications, climate smart agriculture). Constraint network analysis shows that institutional innovation to address government constraints at national level related to poor interaction and collaboration will have a positive impact on constraints faced by other stakeholder groups. We conclude that much of the R4D investments and innovation in the Central Africa Highlands remain targeting household productivity at farm level. Reasons for that include (1) a narrow focus on sustainable intensification, (2) institutional mandates and pre-analytical choices based project objectives and disciplinary bias, (3) short project cycles that impede work on middle- and long-term NRM and institutional innovation, (4) the likelihood that institutional experimentation can become political, and (5) complexity in terms of expanded systems boundaries and measuring impact.
2 Ainembabazi, J. H.; Abdoulaye, T.; Feleke, S.; Alene, A.; Dontsop-Nguezet, P. M.; Ndayisaba, P. C.; Hicintuka, C.; Mapatano, S.; Manyong, V. 2018. Who benefits from which agricultural research-for-development technologies?: evidence from farm household poverty analysis in Central Africa. World Development, 108:28-46. [doi: https://doi.org/10.1016/j.worlddev.2018.03.013]
Agricultural research for development ; Technology assessment ; Innovation adoption ; Farmers ; Households ; Poverty ; Impact assessment ; Social welfare ; Crop production ; Varieties / Central Africa / Burundi / Democratic Republic of the Congo / Rwanda
(Location: IWMI HQ Call no: e-copy only Record No: H048852)
(1.37 MB)
It remains a challenge for agricultural research-for-development (AR4D) institutions to demonstrate to donors which technologies contribute significantly to poverty reduction due to a multitude of impact pathways. We attempt to overcome this challenge by utilizing the potential outcomes framework and quantile treatment effects analytical approaches applied on panel household data collected from Central Africa. Our findings show that adoption of AR4D technologies reduced the probability of being poor by 13 percentage points. A large share of this poverty reduction is causally attributable to adoption of improved crop varieties (32%) followed by adoption of post-harvest technologies (28%) and crop and natural resource management (26%), with the rest 14% attributable to unidentified and/or unmeasured intermediate outcomes or factors. The findings further indicate that relatively poor farm households benefit from adopting improved crop varieties more than the relatively better-off households. Correspondingly, the relatively better off households benefit from adopting post-harvest technologies enhancing crop commercialization much more than the relatively poor households. The findings reveal interesting policy implications for successful targeting of agricultural interventions aimed at reducing rural poverty.
Powered by DB/Text WebPublisher, from
