Prospects for equitable growth in rural sub-Saharan Africa

Improving agricultural technology equitably in Africa has been difficult in the past because of the vast differences, as well as weak institutions and infrastructure in its many regions. However, the prospects for equitable growth are good for several reasons. The distribution of land has not deteriorated, and there are few landless people in Africa. Technical packages do not favor large farms over small ones, and Africa's social institutions support people with a safety net for sources of income. The author, however, points out that equitable growth, though possible is not assured and several research and policy initiatives will be needed to capitalize on the potential. First, research must continue to focus on technology appropriate for small farms and crops. Policy makers must no longer withhold assistance from service enterprises or nonfarm activities of women. Rural infrastructure has to be upgraded, and finally, governments will need to monitor land tenure and tenancy.Economic Theory&Research,Agricultural Research,Crops&Crop Management Systems,Environmental Economics&Policies,Agricultural Knowledge&Information Systems

Rural - urban growth linkages in India

The rural nonfarm economy accounts for one-quarter of all full-time employment in rural India and for nearly one-third of rural income, and is also intimately linked to agriculture. This paper examines the importance of rural-urban growth linkages in India, and aims to assess the impact of agricultural growth on national demand for nonfarm products. In addition, because growing land scarcity raises concerns about prospects for rural labor absorption, the paper highlights the impact of agricultural growth on rural nonfarm incomes and employment.Four major sections address these objectives. The first provides a descriptive overview of nonfarm activity in India. It examines the importance, composition and location of nonfarm activity as well as general trends over the past 30 years. The second explores the relationship between agriculture and changes in nonfarm activity. After reviewing previous growth linkage studies, it compares nonfarm activity in high- and low-productivity agricultural states cross-sectionally and over time. The third section estimates the volume of rural nonfarm income and employment generated by agricultural growth, while the fourth projects patterns of demand for nonfarm goods emanating from alternative agricultural growth scenarios.Environmental Economics&Policies,Economic Theory&Research,Achieving Shared Growth,Governance Indicators,Agricultural Knowledge&Information Systems

Farm-nonfarm linkages in rural sub-Saharan Africa

This paper is an accumulation, over the past 25 years, of a body of detailed work examining the structure of Africa's rural, nonfarm economy. First, it systematically reviews empirical evidence on the nature and magnitude of the African rural, nonfarm economy. It then explores differences across locality and size, across countries and over time, in an effort to assess likely patterns of growth. A subsequent review of key production and consumption parameters allows an estimate of the magnitude of the agricultural growth multipliers in Africa. The paper concludes with a brief discussion of policies and programs that will be necessary if farm-nonfarm growth linkages are to achieve their full potential.Banks&Banking Reform,Agricultural Knowledge&Information Systems,Municipal Financial Management,Environmental Economics&Policies,Economic Theory&Research

Strategies for stimulating poverty-alleviating growth in the rural nonfarm economy in developing countries:

"The rural nonfarm economy (RNFE) accounts for roughly 25 percent of full-time rural employment and 35-40 percent of rural incomes across the developing world. This diverse collection of seasonal trading, household-based and large-scale agroprocessing, manufacturing and service activities plays a crucial role in sustaining rural populations, in servicing a growing and modern agriculture, and in supplying local consumer goods and services. In areas where landlessness prevails, rural nonfarm activity offers important economic alternatives for the rural poor....Three key groups currently intervene in the rural nonfarm economy: large private enterprises, non-profit promotional agencies and governments. Large modern corporations take investment, procurement and marketing decisions that powerfully shape opportunities in the rural nonfarm economy throughout much of the Third World...." The authors put forth three basic principles for policy makers who want to ensure equitable growth of the RNFE : (1) Identify key engines of regional growth; (2) Focus on subsector-specific supply chains; and (3) Build flexible institutional coalitions. They conclude that "a prosperous rural nonfarm economy can contribute to both aggregate economic growth and improved welfare of the rural poor." from Executive Summary.Poverty alleviation Developing countries., Rural population., Employment, Non-agricultural Rural areas., Manufacturing industries., Service industries.,

African agriculture

"African farmers and agricultural policymakers have achieved a series of significant successes in agricultural development, although these successes are still inadequate in number and scale to counter Sub-Saharan Africa's daunting demographic challenge.....What common ingredients and processes underlie these earlier successes? How can policymakers translate these lessons into improved performance going forward? By examining instances in which important advances have occurred in the past in African agriculture, IFPRI, NEPAD, and colleagues aim to identify promising avenues for achieving similar success in the future. The following briefs offer highlights of some of these important accomplishments and lessons learned from past successes in African agriculture. Collectively, they aim to identify key ingredients necessary for building on these individual cases and expanding them into broad-based agricultural growth." From Text

Transforming the rural nonfarm economy: Opportunities and threats in the developing world

"Rural residents across the developing world earn a large share of their income—35–50 percent—from nonfarm activities. Agricultural households count on nonfarm earnings to diversify risk, moderate seasonal income swings, and finance agricultural input purchases, whereas landless and near-landless households everywhere depend heavily on nonfarm income for their survival. Over time, the rural nonfarm economy has grown rapidly, contributing significantly to both employment and rural income growth. Long neglected by policymakers, the rural nonfarm economy has attracted considerable attention in recent years. In poor agrarian countries struggling with growing numbers of marginal farmers and lackluster agricultural performance, such as those in much of Africa, policymakers view the rural nonfarm economy as a potential alternative to agriculture for stimulating rural income growth. In countries whose economies are successfully shifting from agriculture to other sectors, policymakers see the rural nonfarm economy as a sector that can productively absorb the many agricultural workers and small farmers being squeezed out of agriculture by increasingly commercialized and capitalintensive modes of farming. Given frequently low capital requirements in the nonfarm economy, policymakers in both settings view the rural nonfarm economy as offering a potential pathway out of poverty for many of their rural poor. Expectations everywhere are high. How realistic are these expectations? Can the rural nonfarm economy indeed grow rapidly enough to productively absorb a growing rural labor force? And in doing so, can it, in fact, provide a pathway out of poverty for the rural poor? A recent book published for IFPRI by Johns Hopkins University Press and Oxford University Press in India, Transforming the Rural Nonfarm Economy: Opportunities and Threats in the Developing World, marshals empirical evidence from around the globe to explore these key policy questions. The book, edited by Steven Haggblade, Peter B. R. Hazell, and Thomas Reardon, examines key factors affecting growth and equity in the rural nonfarm economy in order to identify settings and policies that favor rural nonfarm growth and enable the poor to participate in growing segments of the evolving rural nonfarm economy." from textAgricultural industries Developing countries, Agriculture Economic aspects Developing countries, Developing countries Rural conditions., Nonfarm economy,

Multiple Peptidoglycan Modification Networks Modulate Helicobacter pylori's Cell Shape, Motility, and Colonization Potential

Helical cell shape of the gastric pathogen Helicobacter pylori has been suggested to promote virulence through viscosity-dependent enhancement of swimming velocity. However, H. pylori csd1 mutants, which are curved but lack helical twist, show normal velocity in viscous polymer solutions and the reason for their deficiency in stomach colonization has remained unclear. Characterization of new rod shaped mutants identified Csd4, a DL-carboxypeptidase of peptidoglycan (PG) tripeptide monomers and Csd5, a putative scaffolding protein. Morphological and biochemical studies indicated Csd4 tripeptide cleavage and Csd1 crosslinking relaxation modify the PG sacculus through independent networks that coordinately generate helical shape. csd4 mutants show attenuation of stomach colonization, but no change in proinflammatory cytokine induction, despite four-fold higher levels of Nod1-agonist tripeptides in the PG sacculus. Motility analysis of similarly shaped mutants bearing distinct alterations in PG modifications revealed deficits associated with shape, but only in gel-like media and not viscous solutions. As gastric mucus displays viscoelastic gel-like properties, our results suggest enhanced penetration of the mucus barrier underlies the fitness advantage conferred by H. pylori's characteristic shape

Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing

Clear cell renal carcinomas (ccRCCs) can display intratumor heterogeneity (ITH). We applied multiregion exome sequencing (M-seq) to resolve the genetic architecture and evolutionary histories of ten ccRCCs. Ultra-deep sequencing identified ITH in all cases. We found that 73–75% of identified ccRCC driver aberrations were subclonal, confounding estimates of driver mutation prevalence. ITH increased with the number of biopsies analyzed, without evidence of saturation in most tumors. Chromosome 3p loss and VHL aberrations were the only ubiquitous events. The proportion of C>T transitions at CpG sites increased during tumor progression. M-seq permits the temporal resolution of ccRCC evolution and refines mutational signatures occurring during tumor development

DESNT: A Poor Prognosis Category of Human Prostate Cancer.

BACKGROUND: A critical problem in the clinical management of prostate cancer is that it is highly heterogeneous. Accurate prediction of individual cancer behaviour is therefore not achievable at the time of diagnosis leading to substantial overtreatment. It remains an enigma that, in contrast to breast cancer, unsupervised analyses of global expression profiles have not currently defined robust categories of prostate cancer with distinct clinical outcomes. OBJECTIVE: To devise a novel classification framework for human prostate cancer based on unsupervised mathematical approaches. DESIGN, SETTING, AND PARTICIPANTS: Our analyses are based on the hypothesis that previous attempts to classify prostate cancer have been unsuccessful because individual samples of prostate cancer frequently have heterogeneous compositions. To address this issue, we applied an unsupervised Bayesian procedure called Latent Process Decomposition to four independent prostate cancer transcriptome datasets obtained using samples from prostatectomy patients and containing between 78 and 182 participants. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Biochemical failure was assessed using log-rank analysis and Cox regression analysis. RESULTS AND LIMITATIONS: Application of Latent Process Decomposition identified a common process in all four independent datasets examined. Cancers assigned to this process (designated DESNT cancers) are characterized by low expression of a core set of 45 genes, many encoding proteins involved in the cytoskeleton machinery, ion transport, and cell adhesion. For the three datasets with linked prostate-specific antigen failure data following prostatectomy, patients with DESNT cancer exhibited poor outcome relative to other patients (p=2.65×10-5, p=4.28×10-5, and p=2.98×10-8). When these three datasets were combined the independent predictive value of DESNT membership was p=1.61×10-7 compared with p=1.00×10-5 for Gleason sum. A limitation of the study is that only prediction of prostate-specific antigen failure was examined. CONCLUSIONS: Our results demonstrate the existence of a novel poor prognosis category of human prostate cancer and will assist in the targeting of therapy, helping avoid treatment-associated morbidity in men with indolent disease. PATIENT SUMMARY: Prostate cancer, unlike breast cancer, does not have a robust classification framework. We propose that this failure has occurred because prostate cancer samples selected for analysis frequently have heterozygous compositions (individual samples are made up of many different parts that each have different characteristics). Applying a mathematical approach that can overcome this problem we identify a novel poor prognosis category of human prostate cancer called DESNT.This work was funded by the Bob Champion Cancer Trust, The Masonic Charitable Foundation successor to The Grand Charity, The King Family, and The University of East Anglia. We acknowledge support from Movember, from Prostate Cancer UK, Callum Barton, and from The Andy Ripley Memorial Fund. The research presented in this paper was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia. Cancer Research UK Grant 10047 funded the generation of the prostate CancerMap expression microarray dataset. We would like to acknowledge the support of the National Institute for Health Research which funds the Cambridge Bio-medical Research Centre, Cambridge UK