A meta-analysis of long-term effects of conservation agriculture on maize grain yield under rain-fed conditions

Conservation agriculture involves reduced tillage, permanent soil cover and crop rotations to enhance soil fertility and to supply food from a dwindling land resource. Recently, conservation agriculture has been promoted in Southern Africa, mainly for maize-based farming systems. However, maize yields under rain-fed conditions are often variable. There is therefore a need to identify factors that influence crop yield under conservation agriculture and rain-fed conditions. Here, we studied maize grain yield data from experiments lasting 5 years and more under rain-fed conditions. We assessed the effect of long-term tillage and residue retention on maize grain yield under contrasting soil textures, nitrogen input and climate. Yield variability was measured by stability analysis. Our results show an increase in maize yield over time with conservation agriculture practices that include rotation and high input use in low rainfall areas. But we observed no difference in system stability under those conditions. We observed a strong relationship between maize grain yield and annual rainfall. Our meta-analysis gave the following findings: (1) 92% of the data show that mulch cover in high rainfall areas leads to lower yields due to waterlogging; (2) 85% of data show that soil texture is important in the temporal development of conservation agriculture effects, improved yields are likely on well-drained soils; (3) 73% of the data show that conservation agriculture practices require high inputs especially N for improved yield; (4) 63% of data show that increased yields are obtained with rotation but calculations often do not include the variations in rainfall within and between seasons; (5) 56% of the data show that reduced tillage with no mulch cover leads to lower yields in semi-arid areas; and (6) when adequate fertiliser is available, rainfall is the most important determinant of yield in southern Africa. It is clear from our results that conservation agriculture needs to be targeted and adapted to specific biophysical conditions for improved impact

SMAR1 binds to T(C/G) repeatvand inhibits tumor progression by regulating miR-371-373 cluster

Chromatin architecture and dynamics are regulated by various histone and non-histone proteins. The matrix attachment region binding proteins (MARBPs) play a central role in chromatin organization and function through numerous regulatory proteins. In the present study, we demonstrate that nuclear matrix protein SMAR1 orchestrates global gene regulation as determined by massively parallel ChIPsequencing. The study revealed that SMAR1 binds to T(C/G) repeat and targets genes involved in diverse biological pathways. We observe that SMAR1 binds and targets distinctly different genes based on the availability of p53. Our data suggest that SMAR1 binds and regulates one of the imperative microRNA clusters in cancer and metastasis, miR-371-373. It negatively regulates miR-371-373 transcription as confirmed by SMAR1 overexpression and knockdown studies. Further, deletion studies indicate that a ~200 bp region in the miR-371-373 promoter is necessary for SMAR1 binding and transcriptional repression. Recruitment of HDAC1/mSin3A complex by SMAR1, concomitant with alteration of histone marks results in downregulation of the miRNA cluster. The regulation of miR-371-373 by SMAR1 inhibits breast cancer tumorigenesis and metastasis as determined by in vivo experiments. Overall, our study highlights the binding of SMAR1 to T(C/G) repeat and its role in cancer through miR-371-37

Toward Smart Implant Synthesis: Bonding Bioceramics of Different Resorbability to Match Bone Growth Rates

This work was partially funded by the European Union (Project MARMED - 2011-1/164), the Spanish Government and FEDER (CICYT MAT2006-10481) by Xunta de Galicia (CN2012/292)

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Not AvailableThe regional climate models provide sufficient information of the climate data, which can be used for simulating the impact of expected climate change on crop growth and hydrological processes. But future climate data derived from such models often suffers from bias and is not ready to use per se in crop growth/hydrological models, wherein reasonable and consistent meteorological daily input data is a crucial factor. The present study concerns the assessment and minimization of the bias in the PRECIS modeled data of maximum and minimum temperatures and rainfall for Ludhiana station, representing central Punjab of India. The correction functions for three corrective methods i.e. difference, modified difference and statistical bias correction at daily, monthly and annual time scales were developed and validated to minimize the bias. Amongst these, correction functions derived using modified difference method at daily time scale for rainfall and at monthly time scale for Tmax and Tmin were found to be the superseding.Not Availabl