Scalability constraints in Disruptive Agricultural Technologies (DATs) along Value Chain on agricultural production in South Sudan
https://doi.org/10.26832/24566632.2021.0603021
Abstract
Scalability known as the capacity of input variables along the Value Chain (VC) to effect transformative changes on agricultural production was evaluated for a farming system in Juba County of Central Equatoria State (CES), South Sudan. These transformative input variables commonly referred to as, Disruptive Agricultural Technologies (DATs) in the form of advisory, material as well as technological variables were shown to positively influence agricultural production from a default state. The objective of this study was to find out how a probability-based Bayesian Belief Network (BBN) software NETICA could be applied to assess as well as upscale the level of agricultural production P(Prodlevel | ) from a data input domain D. Simulation using a 700 kg ha-1 of cowpea yield at 50% Cumulative Probability Distribution (CPD) as a calibrant, the backcasting method showed that, scaling up of marginal probabilities in agrotechnology and financial resources from 0.025 to 0.1 (25% increment) and from 0.015 to 0.03 (50% increment) respectively, while keeping other input variables unchanged, increased cowpea yield from 692.9 to 783.1 kg ha-1 (about 12% increment). Conversely, where no DATs were introduced as in the worst-case scenario, production level was comparatively lower. The BBN model is thus, an indispensable tool that can provide useful information on scaling up agricultural production and hence improve livelihood opportunities in Juba County. However, for sustainable agricultural production, scalability may be constrained by spatial-temporal, environmental and socio-economic imperatives as well as on availability, accessibility, affordability of all input variables.
Keywords:
Backcasting, Bayesian Belief Network, Conditional Probabilities, Disruptive Agricultural Technologies, Input variablesDownloads
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