Raising the stakes: cassava seed networks at multiple scales in Cambodia and Vietnam

Abstract

24Cassava is one of the most important annual crops in Southeast Asia, and faces increasing seed borne 25 pest and disease pressures. Despite this, cassava seed systems have received scant research attention. 26In a first analysis of Vietnamese and Cambodian cassava seed systems, we characterized existing 27 cassava seed systems in 2016-17 through a farmer survey based approach at both national and 28 community scales, with particular focus on identifying seed system actors, planting material 29 management, exchange mechanisms, geographies, and variety use, and performed a network analysis 30 of detected seed movement at the provincial level. Cassava seed -Vietnam and Cambodia 2 commercial sale was more prevalent in high-intensity than in low-intensity production sites. 44Considerable variability existed in local seed networks, depending on the intensity of production and 45 integration with trader networks. Adapted innovations are needed to upgrade cassava seed systems in 46 the face of emerging pests and diseases, taking into account and building on the strengths of the 47 existing systems; including their social nature and ability to quickly and efficiently distribute planting 48 materials at the regional level. 49

User guide to the toolbox for working with root, tuber and banana seed systems. RTB User guide

“…With a grant from the Australian Center for International Agricultural Research (ACIAR), the International Center for Tropical Agriculture (CIAT) led a study with WUR, UF, and national partners in Cambodia, Vietnam, and China to understand the use and movement of cassava seed, and to model likely spread and the impact of mitigation interventions. The approach used a combination of seed tracing (tool 5, Table 1) to map existing plant material exchange networks (Delaquis et al 2018) and impact network analysis (tool 2, Table 1) to construct epidemiological models combining both environmental parameters and seed exchange networks to model spread scenarios (publication forthcoming). The results of this work have led to a large new ACIAR research project on SLCMV response, and have been presented at stakeholder workshops and meetings of the regional taskforces for SLCMV, providing regional government representatives and cassava value chain stakeholders with the first ever regional data on SLCMV, seed exchange, and scenario analyses for planning interventions.…”

Modeling Epidemics in Seed Systems and Landscapes To Guide Management Strategies: The Case of Sweet Potato in Northern Uganda

“…Selection was based on how frequently the pathogen would be detected in these locations before substantial parts of the rest of the network become colonized in simulations. This surveillance score (Buddenhagen et al 2017 ; Garrett et al 2018 ) calculated for villages was highly correlated across intermediate spread parameters when comparing the parameter range both within and between the two dispersal models (both the negative exponential and inverse power law models). The insensitivity of surveillance node selection to dispersal parameter values is an important feature for its potential application across seed systems, suggesting that the exact number of links between locations is not critical for identifying the best locations for surveillance.…”

“…Understanding the dynamics of epidemics in seed systems is critical for effective pathogen monitoring, risk assessment, and epidemic management (Buddenhagen et al 2017 ; Garrett et al 2018 ; Harwood et al 2009 ; Shaw and Pautasso 2014 ). Although some plant disease literature explores this topic, research on epidemics in real-world seed networks is still in an early stage.…”

Global Cropland Connectivity: A Risk Factor for Invasion and Saturation by Emerging Pathogens and Pests

“…The current analysis is based on geographic data for 2000 (Monfreda et al 2008 ) and 2005 (IFPRI and IIASA 2016 ), so areas in which crop densities have increased rapidly in recent years are not represented in these global maps yet. An important example is cassava production in Southeast Asia, where production has quickly expanded and is now experiencing an invasion of Sri Lankan cassava mosaic virus (Wang et al 2015 , Delaquis et al 2018 ). The cropland density data are summarized across global data sets that vary widely in quality from region to region.…”