Roads and Rural Development in Sub-Saharan Africa

Berg, Claudia N.; Blankespoor, Brian; Selod, Harris

doi:10.1080/00220388.2018.1430772

Cited by 45 publications

(29 citation statements)

References 26 publications

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“…In the study area, land use embodied mainly in the form of land use types, road accessibility (RA), and land patch fragmentation are the farmers’ response to CLQ [28,29,30,31]. As paddy fields are the only type of cultivated land in this study, RA (Table 2) and land patch fragmentation were selected as LURIs indicators.…”

Section: Methodsmentioning

confidence: 99%

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Liu

Peng

Xia

et al. 2019

Sensors

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Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

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Section: Methodsmentioning

confidence: 99%

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Liu

Peng

Xia

et al. 2019

Sensors

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“…Small towns and cities are experiencing the most rapid growth in employment in parts of Africa and may therefore be a major catalyst for structural transformation (for example, Christiaensen & Todo, 2014). Relatedly, Berg, Blankespoor, and Selod (2018) show that African road infrastructure expansion is associated with GDP gains which suggest the expansion of rural non-farm activities. However, the causal relationships between farm, downstream agri-food system, and non-farm job growth have yet to be clearly disentangled.…”

Section: How Are African Countries' Transformations Different From Asia?mentioning

confidence: 99%

“…A fourth and ominous distinction with Asia is that Sub-Saharan Africa's agricultural growth still relies mainly on expansion of area under cultivation, not yield growth (Fuglie & Rada, 2013). Improvements in road infrastructure appear to have facilitated cropland expansion in the region (Berg et al, 2018). Area expansion has been accompanied by massive land degradation and soil fertility depletion (Barbier & Hochard, 2016), suggesting unsustainable forms of intensification in much of the region (Drechsel, Gyiele, Kunze, & Cofie, 2001;Tittonell & Giller, 2013).…”

Section: How Are African Countries' Transformations Different From Asia?mentioning

confidence: 99%

Africa’s Unfolding Economic Transformation

Jayne

Chamberlin

Benfica

2018

The Journal of Development Studies

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Despite the continued deep challenges that the region is facing, mounting evidence points to profound economic transformation in sub-Saharan Africa since the early 2000s. The contributions in this special issue highlight three aspects of Africa's unfolding economic transformation since 2000: remarkable progress for the region as a whole, highly uneven progress across countries, and unresolved questions about the sustainability of the transformations. The drivers of the region's economic transformations are diverse, and include improved governance, strong agricultural growth in some countries, employment expansion in informal rural off-farm activities, strong local and foreign investment, a period of high global commodity prices, and policy reforms undertaken in earlier decades. Agricultural growth, by expanding job opportunities in the non-farm sectors through multiplier effects, is likely to remain an important driver of continued transformation, though it will increasingly need to rely on productivity growth rather than area expansion.

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“…Other studies have looked indirectly at the productivity effects of farmers' location with respect to large urban centers, by looking at physical travel distances (e.g., Amare and Shiferaw 2017) or by interacting it with binary market access indicators (e.g., Iimi et al 2018). There is also the extensive literature on the importance of roads for the modernization of the agricultural sector in Africa (Dorosh et al 2012;Berg et al 2018) approach similar to Damania et al (2016) and Vandercasteelen et al (2018b). We use the 'natural path' cost of how long it would take a farmer to walk to an urban center to instrument for the road network-based measurement of travel time.…”

Section: Introductionmentioning

confidence: 99%

“…This paper makes several other contributions to the existing literature. While there is a substantial literature that has linked (improved) access to market and road infrastructure with improved well-being for households in rural areas of developing countries (Stifel and Minten 2017;Berg et al 2018;Nakamura et al 2019), most of this research remains silent on the mechanisms through which market access improves household outcomes. This paper shows that for agricultural households located close to the capital of Ethiopia, access to modern value chains is an important pathway to higher agricultural productivity levels and, hence, to transformation of the agricultural sector.…”

Section: Introductionmentioning

confidence: 99%

Cities, value chains, and dairy production in Ethiopia

Vandercasteelen¹,

Minten²,

Tamru³

2019

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This paper explores the spatial heterogeneity in dairy production in the highland production area around the capital of Ethiopia, Addis Ababa. We look at how urban proximity -defined as the travel time from the farm to the central market of Addis Ababa -affects the production decisions of Ethiopian dairy farmers. We sampled 870 households from the major rural production zones around Addis Ababa, where villages were stratified according to their distance to Addis Ababa. Using an instrumental variable approach, we find evidence of strong spatial heterogeneity in dairy milk productivity in Ethiopia. With each additional hour of travel time, the milk productivity per cow is reduced by almost 1 liter per day, a reduction by 26 percent on average. This spatial heterogeneity in milk productivity reflects a pronounced spatial variation in dairy production decisions (producing liquid milk or processed dairy products), the application of modern inputs, and marketing. When trying to disentangle the mechanisms through which urban proximity affects dairy productivity, we show that the effect of travel time mainly runs through farmers' inclusion into 'modern' value chains and more specifically through their access to commercial milk buyers. This finding holds when we control for prices, indicating that access to commercial value chains are an important determinant of dairy productivity. However, as only a limited number of farmers now have access to such value chains in these settings, measures to make dairy value chains more inclusive to remote farmers can have important economic development benefits for them.

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Roads and Rural Development in Sub-Saharan Africa

Cited by 45 publications

References 26 publications

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Africa’s Unfolding Economic Transformation

Cities, value chains, and dairy production in Ethiopia

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