1000 resultados para Familial agriculture


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A large-scale, outdoor, pervasive computing system based on the Fleck hardware platform applies sensor network technology to farming. Comprising static and animal-borne mobile nodes, the system measures the state of a complex, dynamic system comprising climate, soil, pasture, and animals. This data supports prediction of the land's future state and improved management outcomes through closed-loop control. This article is part of a special issue, Building a Sensor-Rich World.

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Agriculture's contribution to radiative forcing is principally through its historical release of carbon in soil and vegetation to the atmosphere and through its contemporary release of nitrous oxide (N2O) and methane (CHM4). The sequestration of soil carbon in soils now depleted in soil organic matter is a well-known strategy for mitigating the buildup of CO2 in the atmosphere. Less well-recognized are other mitigation potentials. A full-cost accounting of the effects of agriculture on greenhouse gas emissions is necessary to quantify the relative importance of all mitigation options. Such an analysis shows nitrogen fertilizer, agricultural liming, fuel use, N2O emissions, and CH4 fluxes to have additional significant potential for mitigation. By evaluating all sources in terms of their global warming potential it becomes possible to directly evaluate greenhouse policy options for agriculture. A comparison of temperate and tropical systems illustrates some of these options.

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Nitrous oxide (N2O) is a major greenhouse gas (GHG) product of intensive agriculture. Fertilizer nitrogen (N) rate is the best single predictor of N2O emissions in row-crop agriculture in the US Midwest. We use this relationship to propose a transparent, scientifically robust protocol that can be utilized by developers of agricultural offset projects for generating fungible GHG emission reduction credits for the emerging US carbon cap and trade market. By coupling predicted N2O flux with the recently developed maximum return to N (MRTN) approach for determining economically profitable N input rates for optimized crop yield, we provide the basis for incentivizing N2O reductions without affecting yields. The protocol, if widely adopted, could reduce N2O from fertilized row-crop agriculture by more than 50%. Although other management and environmental factors can influence N2O emissions, fertilizer N rate can be viewed as a single unambiguous proxy—a transparent, tangible, and readily manageable commodity. Our protocol addresses baseline establishment, additionality, permanence, variability, and leakage, and provides for producers and other stakeholders the economic and environmental incentives necessary for adoption of agricultural N2O reduction offset projects.

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Abstract Causative genetic variants have to date been identified for only a small proportion of familial colorectal cancer (CRC). While conditions such as Familial Adenomatous Polyposis and Lynch syndrome have well defined genetic causes, the search for variants underlying the remainder of familial CRC is plagued by genetic heterogeneity. The recent identification of families with a heritable predisposition to malignancies arising through the serrated pathway (familial serrated neoplasia or Jass syndrome) provides an opportunity to study a subset of familial CRC in which heterogeneity may be greatly reduced. A genome-wide linkage screen was performed on a large family displaying a dominantly-inherited predisposition to serrated neoplasia genotyped using the Affymetrix GeneChip Human Mapping 10 K SNP Array. Parametric and nonparametric analyses were performed and resulting regions of interest, as well as previously reported CRC susceptibility loci at 3q22, 7q31 and 9q22, were followed up by finemapping in 10 serrated neoplasia families. Genome-wide linkage analysis revealed regions of interest at 2p25.2-p25.1, 2q24.3-q37.1 and 8p21.2-q12.1. Finemapping linkage and haplotype analyses identified 2q32.2-q33.3 as the region most likely to harbour linkage, with heterogeneity logarithm of the odds (HLOD) 2.09 and nonparametric linkage (NPL) score 2.36 (P = 0.004). Five primary candidate genes (CFLAR, CASP10, CASP8, FZD7 and BMPR2) were sequenced and no segregating variants identified. There was no evidence of linkage to previously reported loci on chromosomes 3, 7 and 9.

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This paper uses an aggregate quantity space to decompose the temporal changes in nitrogen use efficiency and cumulative exergy use efficiency into changes of Moorsteen–Bjurek (MB) Total Factor Productivity (TFP) changes and changes in the aggregate nitrogen and cumulative exergy contents. Changes in productivity can be broken into technical change and changes in various efficiency measures such as technical efficiency, scale efficiency and residual mix efficiency. Changes in the aggregate nitrogen and cumulative exergy contents can be driven by changes in the quality of inputs and outputs and changes in the mixes of inputs and outputs. Also with cumulative exergy content analysis, changes in the efficiency in input production can increase or decrease the cumulative exergy transformity of agricultural production. The empirical study in 30 member countries of the Organisation for Economic Co-operation Development from 1990 to 2003 yielded some important findings. The production technology progressed but there were reductions in technical efficiency, scale efficiency and residual mix efficiency levels. This result suggests that the production frontier had shifted up but there existed lags in the responses of member countries to the technological change. Given TFP growth, improvements in nutrient use efficiency and cumulative exergy use efficiency were counteracted by reductions in the changes of the aggregate nitrogen contents ratio and aggregate cumulative exergy contents ratio. The empirical results also confirmed that different combinations of inputs and outputs as well as the quality of inputs and outputs could have more influence on the growth of nutrient and cumulative exergy use efficiency than factors that had driven productivity change. Keywords: Nutrient use efficiency; Cumulative exergy use efficiency; Thermodynamic efficiency change; Productivity growth; OECD agriculture; Sustainability