Methodological innovations in estimating the (inverse) relationship between farm productivity and farm size in a developing economy: a case study of Burundi

We use a nonparametric estimation of the production function to investigate the relationship between farm productivity and farming scale in poor smallholder agricultural systems in the north of Burundi. Burundi is one of the poorest countries in the world, with a predominant small scale subsistence farming sector. A Kernel regression is used on data of mixed cropping systems to study the determinants of production including different factors that have been identified in literature as missing variables in the testing of the inverse relationship such as soil quality, location and household heterogeneity. Household data on farm activities and crop production was gathered among 640 households in 2007 in two Northern provinces of Burundi. Four production models were specified each with different control variables. For the relatively small farms, we find clear evidence of an inverse relationship. The relatively large farms show a different pattern. Returns to scale are found to be farm scale dependent. Parametric Cobb-Douglass models tend to over-simplify the debate on returns to scale because of not accounting for the different effects of large farms. Other factors that significantly positively affect production include the soil quality and production orientation towards banana or cash crop production. Production seems to be negatively affected by field fragmentation.inverse relationship, farm size, nonparametric, Burundi, Agricultural and Food Policy, Community/Rural/Urban Development, Environmental Economics and Policy, D24, O13, Q12, Q18,

Challenging Small-scale Farming, A Non-parametric Analysis of the (Inverse) Relationship Between Farm Productivity and Farm Size in Burundi

We use a nonparametric estimation of the production function to investigate the relation- ship between farm productivity and farming scale in poor smallholder agricultural systems in the north of Burundi. Burundi is one of the poorest countries in the world, with a predominant small scale subsistence farming sector. A Kernel regression is used on data of mixed cropping systems to study the determinants of production including different factors that have been identified in literature as missing variables in the testing of the inverse relationship such as soil quality, location and household heterogeneity. Household data on farm activities and crop production was gathered among 640 households in 2007 in two Northern provinces of Burundi. Four production models were specified each with different control variables. For the relatively small farms, we find clear evidence of an inverse relationship. The relatively large farms show a different pattern. Returns to scale are found to be farm scale dependent. Parametric Cobb-Douglass models tend to over-simplify the debate on returns to scale because of not accounting for the different effects of large farms. Other factors that significantly positively affect production include the soil quality and production orientation towards banana or cash crop production. Production seems to be negatively affected by field fragmentation.inverse relationship, farm size, nonparametric, Burundi, Farm Management, Productivity Analysis,

An overview of offset analgesia and the comparison with conditioned pain modulation : a systematic literature review

Background: Offset analgesia (OA) is an increasingly described phenomenon to measure endogenous pain inhibition, in which a greater decrease in pain intensity is experienced than would be predicted by the decrease in painful stimulation. The temporal filtering in this OA phenomenon differs from the spatial filtering in the commonly described conditioned pain modulation (CPM). Yet, the knowledge on the efficacy of OA in chronic pain patients is scarce, compared to CPM efficacy. Objective: This systematic review has been conducted to provide an overview of the current knowledge regarding OA, and to compare it to CPM. Study Design: A systematic review of research studies that investigated the application or mechanisms of OA. Setting: The present study took place at Ghent University and the University of Antwerp. Methods: This systematic review follows the PRISMA guidelines. The electronic databases Pubmed and Web of Science were searched in January 2015. Full text clinical reports addressing OA were included. The checklists for randomized controlled trials, case-control studies, and cohort-studies provided by the Dutch Institute for Healthcare Improvement and the Dutch Cochrane Centre were used to assess methodological quality. The articles received a level of evidence A1, A2, B, C, or D, based on study design and risk of bias. These levels were used to determine the strength of conclusion (level 1 to 4). Results: Seventeen articles met the inclusion criteria. Sixteen studies used quantitative sensory testing to provoke OA; however, differences in protocols are present. OA can function as a non-opioid mediated assessment tool for endogenous pain inhibition, and activates brain regions such as periaqueductal gray (PAG), dorsolateral prefontral cortex, insula, medulla, pons and cerebellum, indicating strong brain derived pain modulation. The primary somatosensory cortex is, conversely, less activated during OA. OA is decreased in neuropathic patients. Nonetheless, evidence for the influence of individual factors on OA is limited. OA and CPM seem to rely on different mechanisms. Limitations: Search strategy was taken wide, wherefore a large variety of research perspectives were included. Conclusions: This systematic review displays OA as a temporal filtering mechanisms that is more brain-derived compared to the spatial assessment method CPM. There is strong evidence for reduced OA in neuropathic patients, however, evidence regarding OA in (sub) acute and central sensitization patients, and the influence of personal factors on OA is currently scarce and needs further investigation

Challenging small-scale farming: a non-parametric analysis of the (inverse) relationship between farm productivity and farm size in Burundi

We use a non-parametric approach to investigate the (inverse) relationship between farm productivity and farm size. A kernel regression is used on data of mixed cropping systems to study the determinants of production including different factors that have been identified in literature as missing variables in the testing of the inverse relationship such as soil quality, location, and household heterogeneity. Household data on farm activities and crop production were gathered among 640 households in 2007 in two Northern provinces of Burundi. Our results do not reject the findings of an inverse relationship between farm size and productivity. However, we find that size returns vary substantially with farm size, that is, between 0.2 for the smallest farms and 0.8 for the largest farms. Other factors that affect significantly production include soil quality. Finally, we find a significant positive association between food security and farm size

Methodological innovations in estimating the (inverse) relationship between farm productivity and farm size in a developing economy: a case study of Burundi

We use a nonparametric estimation of the production function to investigate the relationship between farm productivity and farming scale in poor smallholder agricultural systems in the north of Burundi. Burundi is one of the poorest countries in the world, with a predominant small scale subsistence farming sector. A Kernel regression is used on data of mixed cropping systems to study the determinants of production including different factors that have been identified in literature as missing variables in the testing of the inverse relationship such as soil quality, location and household heterogeneity. Household data on farm activities and crop production was gathered among 640 households in 2007 in two Northern provinces of Burundi. Four production models were specified each with different control variables. For the relatively small farms, we find clear evidence of an inverse relationship. The relatively large farms show a different pattern. Returns to scale are found to be farm scale dependent. Parametric Cobb-Douglass models tend to over-simplify the debate on returns to scale because of not accounting for the different effects of large farms. Other factors that significantly positively affect production include the soil quality and production orientation towards banana or cash crop production. Production seems to be negatively affected by field fragmentation

Challenging Small-scale Farming, A Non-parametric Analysis of the (Inverse) Relationship Between Farm Productivity and Farm Size in Burundi

We use a nonparametric estimation of the production function to investigate the relation- ship between farm productivity and farming scale in poor smallholder agricultural systems in the north of Burundi. Burundi is one of the poorest countries in the world, with a predominant small scale subsistence farming sector. A Kernel regression is used on data of mixed cropping systems to study the determinants of production including different factors that have been identified in literature as missing variables in the testing of the inverse relationship such as soil quality, location and household heterogeneity. Household data on farm activities and crop production was gathered among 640 households in 2007 in two Northern provinces of Burundi. Four production models were specified each with different control variables. For the relatively small farms, we find clear evidence of an inverse relationship. The relatively large farms show a different pattern. Returns to scale are found to be farm scale dependent. Parametric Cobb-Douglass models tend to over-simplify the debate on returns to scale because of not accounting for the different effects of large farms. Other factors that significantly positively affect production include the soil quality and production orientation towards banana or cash crop production. Production seems to be negatively affected by field fragmentation

Genome-wide methylome stability and parental effects in the worldwide distributed Lombardy poplar

Abstract Background Despite the increasing number of epigenomic studies in plants, little is known about the forces that shape the methylome in long-lived woody perennials. The Lombardy poplar offers an ideal opportunity to investigate the impact of the individual environmental history of trees on the methylome. Results We present the results of three interconnected experiments on Lombardy poplar. In the first experiment, we investigated methylome variability during a growing season and across vegetatively reproduced generations. We found that ramets collected over Europe and raised in common conditions have stable methylomes in symmetrical CG-contexts. In contrast, seasonal dynamics occurred in methylation patterns in CHH context. In the second experiment, we investigated whether methylome patterns of plants grown in a non-parental environment correlate with the parental climate. We did not observe a biological relevant pattern that significantly correlates with the parental climate. Finally, we investigated whether the parental environment has persistent carry-over effects on the vegetative offspring’s phenotype. We combined new bud set observations of three consecutive growing seasons with former published bud set data. Using a linear mixed effects analysis, we found a statistically significant but weak short-term, parental carry-over effect on the timing of bud set. However, this effect was negligible compared to the direct effects of the offspring environment. Conclusions Genome-wide cytosine methylation patterns in symmetrical CG-context are stable in Lombardy poplar and appear to be mainly the result of random processes. In this widespread poplar clone, methylation patterns in CG-context can be used as biomarkers to infer a common ancestor and thus to investigate the recent environmental history of a specific Lombardy poplar. The Lombardy poplar shows high phenotypic plasticity in a novel environment which enabled this clonal tree to adapt and survive all over the temperate regions of the world