scholarly journals Nonadditive Effects on Decomposition of a Mixture of Rice Straw and Groundnut Stover Applied to a Sandy Soil

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1030
Author(s):  
Wimonsiri Pingthaisong ◽  
Patma Vityakon

Rice straw is an abundant resource, but its use as a sandy soil amendment does not increase soil organic matter (SOM) accumulation. Our study aimed to determine the altered decomposition processes that result from mixing rice straw (RS) (low N, high cellulose) with groundnut stover (GN) (high N) relative to applying these residues singly to a sandy soil to identify the mechanisms underlying decomposition of the mixed residues. A microcosm experiment using the litter bag technique showed synergistic, nonadditive effects (observed < predicted values) of residue mass remaining (31.1% < 40.3% initial) that were concomitant with chemical constituent loss, including C (cellulose, lignin) and N. The nonadditive effects of soil microbiological parameters in response to the applied residues were synergistic (observed > predicted values) for microbial biomass C (MBC) (92.1 > 58.4 mg C kg−1 soil) and antagonistic (observed < predicted values) for microbial metabolic quotient (i.e., the inverse of microbial C use efficiency (CUE)) (0.03 < 0.06 mmol CO2-C • mmol MBC−1 • hr−1) and N mineralization (14.8 < 16.0 mg N kg−1 soil). In the early stage of decomposition (0–14 days), mixed residues increased MBC relative to the single residues, while they decreased N mineralization relative to single GN (p ≤ 0.05). These results indicate an increase in microbial substrate CUE and N use efficiency (NUE) in the mixed residues relative to the single residues. This increased efficiency provides a basis for the synthesis of microbial products that contribute to the formation of the stable SOM pool. The SOM stabilization could bring about the SOM accumulation that is lacking under the single-RS application.

2018 ◽  
Vol 192 ◽  
pp. 02028
Author(s):  
Hassan Zulkifli Abu ◽  
Ibrahim Aniza ◽  
Mohamad Nor Norazman

Small-scale blast tests were carried out to observe and measure the influence of sandy soil towards explosive blast intensity. The tests were to simulate blast impact imparted by anti-vehicular landmine to a lightweight armoured vehicle (LAV). Time of occurrence of the three phases of detonation phase in soil with respect to upward translation time of the test apparatus were recorded using high-speed video camera. At the same time the target plate acceleration was measured using shock accelerometer. It was observed that target plate deformation took place at early stage of the detonation phase before the apparatus moved vertically upwards. Previous data of acceleration-time history and velocity-time history from air blast detonation were compared. It was observed that effects of soil funnelling on blast wave together with the impact from soil ejecta may have contributed to higher blast intensity that characterized detonation in soil, where detonation in soil demonstrated higher plate velocity compared to what occurred in air blast detonation.


Author(s):  
Benedict Irwin ◽  
Thomas Whitehead ◽  
Scott Rowland ◽  
Samar Mahmoud ◽  
Gareth Conduit ◽  
...  

More accurate predictions of the biological properties of chemical compounds would guide the selection and design of new compounds in drug discovery and help to address the enormous cost and low success-rate of pharmaceutical R&D. However this domain presents a significant challenge for AI methods due to the sparsity of compound data and the noise inherent in results from biological experiments. In this paper, we demonstrate how data imputation using deep learning provides substantial improvements over quantitative structure-activity relationship (QSAR) machine learning models that are widely applied in drug discovery. We present the largest-to-date successful application of deep-learning imputation to datasets which are comparable in size to the corporate data repository of a pharmaceutical company (678,994 compounds by 1166 endpoints). We demonstrate this improvement for three areas of practical application linked to distinct use cases; i) target activity data compiled from a range of drug discovery projects, ii) a high value and heterogeneous dataset covering complex absorption, distribution, metabolism and elimination properties and, iii) high throughput screening data, testing the algorithm’s limits on early-stage noisy and very sparse data. Achieving median coefficients of determination, R, of 0.69, 0.36 and 0.43 respectively across these applications, the deep learning imputation method offers an unambiguous improvement over random forest QSAR methods, which achieve median R values of 0.28, 0.19 and 0.23 respectively. We also demonstrate that robust estimates of the uncertainties in the predicted values correlate strongly with the accuracies in prediction, enabling greater confidence in decision-making based on the imputed values.


2019 ◽  
Vol 206 (2) ◽  
pp. 296-307 ◽  
Author(s):  
Eric Oppong Danso ◽  
Adam Yakubu ◽  
Emmanuel Arthur ◽  
Edward B. Sabi ◽  
Stephen Abenney‐Mickson ◽  
...  

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1391
Author(s):  
Xizi Wang ◽  
Svend Christensen ◽  
Jesper Svensgaard ◽  
Signe M. Jensen ◽  
Fulai Liu

There is an urgent need among plant breeders for a deeper understanding of the links between wheat genotypes and their ability to utilize light for biomass production and their efficiency at converting the biomass into grain yield. This field trail was conducted to investigate the variations in radiation use efficiency (RUE) and harvest index (HI) of four spring wheat cultivars grown on two soil types with two nitrogen (N) fertilization levels. Grain yield (GY) was significantly higher with 200 kg N ha−1 than 100 kg N ha−1 and on clay soil than on sandy soil, and a similar trend was observed for shoot dry matter (DM) at maturity. RUE and HI was neither affected by cultivar nor N-fertilization, but was affected by soil type, with a significantly higher RUE and HI on clay than on sandy soil. The differences of water holding capacity between the two soil types was suggested to be a major factor influencing RUE and HI as exemplified by the principal component analysis. Thus, to achieve a high RUE and/or HI, sustaining a good soil water status during the critical growth stages of wheat crops is essential, especially on sandy soils with a low water holding capacity.


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