Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method

Diao, Xinshen; You, Liangzhi; Alpuerto, Vida; Folledo, Renato

doi:10.5772/47938

Cited by 6 publications

(9 citation statements)

References 1 publication

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“…Then, all parameters of the land component are combined simultaneously. Spatial analysis is generated by the result of map overlay related to variation of productivity value (Álvarez-López et al, 2008;Diao et al, 2012;Jiang et al, 2013;Legendre, 1993;Saroinsong et al, 2007). The analysis of variation productivity patterns is based on land component typology that provides information of productivity levels in each typology (Kumar & Manimannan, 2014;Jiang et al, 2013).…”

Section: Approach and Analysismentioning

confidence: 99%

“…The distribution of productivity value toward physiographic types of rice field area illustrated as following figure (Figure 10). However, to know related factors that influence the difference of productivity level of rice field within same physiographic typology need to be analyzed by physical component deeply (Álvarez-López et al, 2008;Diao et al, 2012;Jiang et al, 2013;Legendre, 1993;Saroinsong et al, 2007). Furthermore, that factors should be studied comprehensively in particular correlated to others external factor, such as sociocultural and economic influence (Kumar & Manimannan, 2014;Mahananto, 2009;Ogale & Nagarale, 2014).…”

Section: Distribution Of Rice Field Productivity According To Land Phmentioning

confidence: 99%

“…The spatial pattern shows the distribution of rice field productivity tends most likely to be clustered based on the similarity of physiographic type (Álvarez-López et al, 2008;Diao et al, 2012). Statistically, it has p-value <0.01 and z-score >2.58 (239.26) correspond to Spatial Autocorrelation (Moran's I).…”

Section: Spatial Pattern Of Rice Field Productivitymentioning

confidence: 99%

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Spatial Pattern of Rice Field Productivity Based on Physical Characteristics of Landscape in Citarum Watershed, West Java, Indonesia

Purwono

Aprianto

2018

Geoplanning: Journal of Geomatics and Planning

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This research analyse the pattern of rice field productivity. It was identified from the characteristics of the land component based on physiographic condition through landscape perspective. Identification of productivity pattern has been done partially based on each typology of land components into several segment of the Citarum watershed, West Java Province, Indonesia. Spatial autocorrelation through GIS tool was used as the method in this research. By using moran’s I (index) measurement, degree of dependency of these variables are generated to find the spatial pattern. The result of this study is separated the value of productivity based on segments of watershed, the values of the average of productivity are upstream (6.39 ton/Ha), middle stream (6.52 ton/Ha), and downstream (7.17 ton/Ha), sequentially. The highest productivity is in the downstream area (9.83 ton/Ha) and the lowest is in the upstream area (4.55 ton/Ha). In accordance with physiographic typology showed the rice field in the middle stream has more variation than the upstream or teh downstream area. The highest of average rice field productivity is on alluvial plain. Overall, the rice field productivity on the hills is higher rather that other types of landform the stuctural formation is more dominant, in addition. The spatial pattern shows the distribution of rice field productivity most likely to clustered based on the similarity of physiographic type. Statistically, it has p-value <0.01 and z-score >2.58 (239.26) correspond to Spatial Autocorrelation (Moron’s I). This positive value means a less than 1% likelihood that this clustered pattern could be result of random choice, which the rice field productivity value has similar pattern to others. Thus, it can be generated that the pattern of rice field productivity has a very close relation with the physical characteristics which associated of each typology of land components.

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Section: Approach and Analysismentioning

confidence: 99%

Section: Distribution Of Rice Field Productivity According To Land Phmentioning

confidence: 99%

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Spatial Pattern of Rice Field Productivity Based on Physical Characteristics of Landscape in Citarum Watershed, West Java, Indonesia

Purwono

Aprianto

2018

Geoplanning: Journal of Geomatics and Planning

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“…The semi-desert is the extreme southeast in and around the Ilemi Triangle; it is an extension of the northeastern Kenya semi-arid zone and shares much of the fauna and flora from that region. The lowland forest is the northernmost extension of the Congo Basin forests [122], [120]. However, the Livelihood Profile Project divided the country into seven livelihood zones that are defined based on climate conditions and farming systems: Eastern Flood Plains, Greenbelt, Hills and Mountains, Ironstone Plateau, Nile-Sobat Rivers, Pastoral and Western Flood Plains [121].…”

Section: Farming Systems In South Sudanmentioning

confidence: 99%

“…The country is naturally endowed with agricultural potential given its favourable soil, water and climatic conditions. It is estimated that about 70% of total land area is suitable for producing a wide range of agricultural products, including annual crops such as grains, vegetables, tree crops such as coffee, tea and fruits, livestock, fishery and various forest products [122].…”

Section: Farming Systems In South Sudanmentioning

confidence: 99%

Agricultural Systems in IGAD Region — A Socio-Economic Review

Babikir¹,

Muchina²,

Sebsibe³

et al. 2015

Agroecology

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Scalability constraints in Disruptive Agricultural Technologies (DATs) along Value Chain on agricultural production in South Sudan

Lomeling

2021

Arch. Agric. Environ. Sci.

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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.

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Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method

Cited by 6 publications

References 1 publication

Spatial Pattern of Rice Field Productivity Based on Physical Characteristics of Landscape in Citarum Watershed, West Java, Indonesia

Spatial Pattern of Rice Field Productivity Based on Physical Characteristics of Landscape in Citarum Watershed, West Java, Indonesia

Agricultural Systems in IGAD Region — A Socio-Economic Review

Scalability constraints in Disruptive Agricultural Technologies (DATs) along Value Chain on agricultural production in South Sudan

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