SeedSAGE (Seed System Analysis and Guidance Engine): Digitalizing Seed Systems Resilience Assessments
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Keywords:
SeedSAGE, Seed systems resilience, Seed system in crisis affected communitiesAbstract
In protracted crisis situations, robust seed systems are vital for ensuring food security and mitigating the impacts of stressors such as conflict, climate change, and economic shocks. Traditional Seed Systems Resilience Assessment (SSRA) methods are extremely valuable, but often face challenges in data collection, analysis, and reporting, which hinder timely and effective interventions. This proposed digital SSRA suite, named SeedSAGE (Seed System Analysis and Guidance Engine) is an innovative plan by integrating offline data capture capabilities with robust analytical tools, specifically designed to address the unique challenges of remote and crisis-affected regions. The suite will comprise of three interconnected tools: the Crop Diversity Assessment Tool, the Resilience Analysis Tool, and the Seed Network Analysis Tool. These tools, combined with robust data processing capabilities, will generate tailored reports to empower governments and development agencies to make informed decisions and implement targeted interventions, using AI parsing. The development plan outlines key components, including user-friendly input modules designed for field use, secure cloud-based data storage, and a phased implementation timeline. The tool does not yet exist, but this document will enumerate intricate pathways for easy development. This digital transformation promises to improve data quality, enhance time efficiency, and broaden the reach of assessments. Ultimately, it aims to enhance decision-making processes, bolstering food security and resilience in vulnerable communities. We invite stakeholders, investors, and development agencies to partner with us in this transformative endeavor.
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References
MCGUIRE S & SPERLING L (2016). Seed systems
smallholder farmers use. Food Security, 8(1): 179-195. https:/
/doi. org/10. 1007/s12571-015-0528-8
SUBEDI A, VAN UFFELEN G & MALKOWSKY C (2020).
Building seed system resilience in protracted crisis situations:
Seed system resilience assessment and facilitation tool
(SSRA-FT). Wageningen Centre for Development Innovation,
Wageningen University & Research.
CAEYERS B, CHALMERS N & DE WEERDT J (2012).
Improving consumption measurement and other survey data
through CAPI: Evidence from a randomized experiment.
Journal of Development Economics, 98(1): 19-33.
WOLFERT S, GE L, VERDOUW C, & BOGAARDT M J (2017).
Big Data in smart farming – A review. Agricultural Systems,
: 69-80. https://doi. org/10. 1016/j. agsy. 2017. 01. 023
NGALAMU T, SUBEDI A, & VAN UFFELEN G (2021). Seed
system resilience assessment in Ikwoto County, South Sudan;
Food and Nutrition Security Resilience Programme (REPRO)
South Sudan Programme. Wageningen Centre for
Development Innovation, Wageningen University & Research.
https://doi. org/10. 18174/575684
KOGAN S, LEVIN D, ROUTLEDGE B R, SAGI J S & SMITH
N A (2009). Predicting risk from financial reports with
regression. In Proceedings of Human Language Technologies:
The 2009 Annual Conference of the North American Chapter
of the Association for Computational Linguistics, pp-272-280.
CHRISTOPHER E B, KELSEY F A & JAMES C F (2018). Data
collection for seed system network analysis. https://doi. org/
7287/peerj. preprints. 2806v2
HUNT A, & SPECHT D (2019). Crowdsourced mapping in
crisis zones: Collaboration, organization, and impact.
International Journal of Humanitarian Action, 4: 1. https://doi.
org/10. 1186/s41018-018-0048-1
QADIR J, ALI A, RASOOL R U et al (2016). Crisis analytics:
Big data-driven crisis response. International Journal of
Humanitarian Action, 1: 12. https://doi. org/10. 1186/s41018-
-0013-9
STHAPIT B R, GAUCHAN D, STHAPIT S R, GHIRMIRE K H,
JOSHI B K, JARVIS D & HERRLE J (2017). A field guide to
participatory methods for sourcing new crop diversity. NARC/
LI-BIRD/Bioversity International. 4 p. Permalink. https://hdl.
handle. net/10568/91997
DE BOEF W S, & THIJSSEN M H (2007). Participatory tools
working with crops, varieties, and seeds: A guide for
professionals applying participatory approaches in
agrobiodiversity management, crop improvement and seed
sector development. Wageningen, Wageningen International,
pp. https://www. researchgate. net/publication/25332826
_Participatory_tools_working_with_crops_varieties_and_
seeds_Guide_book_for_professionals_working_agrobiodivers
ity_conservation_participatory_plant_breeding_and_informal
_seed_sector_development Accessed May 2023
POUDEL D, STHAPIT B & SHRESTHA P (2015). An analysis
of social seed network and its contribution to on-farm
conservation of crop genetic diversity in Nepal. https://doi. org/
1155/2015/312621
LOUWAARS N P & MANICAD G (2022). Seed systems
resilience—An overview. Seeds, 1(4): 340-356. https://doi. org/
3390/seeds1040028
ZAMASIYA B, NYIKAHADZOI K & MUKAMURI B B (2017).
Factors influencing smallholder farmers’ behavioural intention
towards adaptation to climate change in transitional climatic
zones: A case study of Hwedza District in Zimbabwe. Journal
of Environmental Management, 198: 233-239. https://doi. org/
1016/j. jenvman. 2017. 04. 073
BONDI A B (2000). Characteristics of scalability and their
impact on performance. Proceedings of the 2nd International
Workshop on Software and Performance (WOSP ’00), 195-
Association for Computing Machinery. https://doi. org/
1145/350391. 350432
ABER J L, TUBBS DOLAN C, KIM H Y & BROWN L (2021).
Children’s learning and development in conflict- and crisisaffected countries: Building a science for action. Development
and Psychopathology, 33(2): 506–521. https://doi. org/10.
/S0954579420001789
BAUMGART M, ROMER L, LUHR M, ROSCHKE C, RITTER
M & PLATTE B (2021). An iterative data cleansing and
migration framework for research information systems. IEEE.
https://doi. org/10. 1109/ICETCI53161. 2021. 9563602
SARGENT J & MICHAEL K (2005). The need for a digital aid
framework in humanitarian relief. University of Wollongong.
https://ro. uow. edu. au/infopapers/377
SHIRALI G & NEMATPOUR L (2019). Evaluation of resilience
engineering using super decisions software. Health Promotion
Perspectives, 9: 191-197.
HEEMSKERK W, DE BOEF W S, SUBEDI A, AUDETBÉLANGER G & GILDEMACHER P (2013). Seed intervention
landscape analysis. ISSD Technical Notes Issue no 4. Centre
for Development Innovation Wageningen UR, Wageningen &
Royal Tropical Institute, Amsterdam.
KENNEDY G, WANG Z, MAUNDU P & HUNTER D (2022).
The role of traditional knowledge and food biodiversity to
transform modern food systems. Trends in Food Science &
Technology. https://doi. org/10. 1016/j. tifs. 2022. 09. 011
TORFI A, SHIRVANI R, KENESHLOO Y, TAVVAF N & FOX E
(2020). Natural language processing advancements by deep
learning: A survey. ArXiv. https://arxiv. org/abs/2003. 01200
FADDA C & ETTEN J (2018). Generating Farm-Validated
Variety Recommendations for Climate Adaptation. The
Climate-Smart Agriculture Papers. https://doi. org/10. 1007/
-3-319-92798-5_11
ETTEN J, SOUSA K, AGUILAR A, BARRIOS M, COTO A,
DELL ACQUA M, FADDA C, GEBREHAWARYAT Y, GEVEL
J, GUPTA A, KIROS A, MADRIZ B, MATHUR P, MENGISTU
D, MERCADO L, MOHAMMED J, PALIWAL A, PÈ M, QUIROS
C, ROSAS J, SHARMA N, SINGH S, SOLANKI I & STEINKE
J (2019). Crop variety management for climate adaptation
supported by citizen science. Proceedings of the National
Academy of Sciences of the United States of America, 116:
- 4199. https://doi. org/10. 1073/pnas. 1813720116
MIJATOVIC D, OUDENHOVEN F, EYZAGUIRRE P &
HODGKIN T (2013). The role of agricultural biodiversity in
strengthening resilience to climate change: Towards an
analytical framework. International Journal of Agricultural
Sustainability, 11(2): 107-95. https://doi. org/10. 1080/
2012. 691221
TSOUMAS I, GIANNARAKIS G, SITOKONSTANTINOU V,
KOUKOS A, LOKA D, BARTSOTAS N, KONTOES C &
ATHANASIADIS I (2022). Evaluating digital tools for
sustainable agriculture using causal inference. ArXiv. https://
doi. org/10. 48550/arXiv. 2211. 03195