•Farm level data were used to compare yields of cereals and break crops with wheat.•Wheat was the highest yielding and least variable crop.•All break -crops were more sensitive to low yielding ...environments than wheat.•Canola was the most consistent broadleaf break crop.•In order to increase species diversity yields need to be improved relative to wheat.
Typically dryland rain fed cropping systems in much of the world (Australia, North America, Europe and the Middle East) are dominated by cereals, such as wheat, even though the rotational benefits of other crops are well documented. Presumably farmers see an advantage to wheat and this paper tested the hypothesis that on-farm break-crop yields are lower compared to wheat, and more variable. Farm level yields for wheat, barley, hay, oats, triticale, canola, lupin, chickpea, and field pea were taken from a survey of Western Australian grain farms (180–302 farms per year). In general, the yield of broad leaf crops was less than cereals and more variable. Additional data were sourced from published experiments and then yields were compared with wheat using linear regression with wheat as the x-axis and break crop as the y-axis. All break -crops were more sensitive to low yielding environments than wheat, as indicated by a positive intercept with the x-axis. Barley and wheat yields were closely related (R2 = 0.63), but in environments where yield was less than 1.25 t/ha wheat tended to yield more, while barley out yielded wheat when yields were greater than 1.25 t/ha. There was a poor relationship between either oat grain (R2 = 0.26) or hay (R2 = 0.10) yields and wheat indicating that these maybe potential income diversification options. Canola was the least variable (R2 = 0.54) broadleaf crop compared to wheat, with an intercept of 0.15 t/ha and for every 1 t/ha increase in wheat yield, canola yield increased by 0.56 t/ha. If increased diversity of crop species on-farm is the objective, then varieties and management packages that increase the yield and reliability of broadleaf crops relative to wheat will need to be developed.
One mechanism for airway closure in the lung is the surface-tension-driven instability of the mucus layer which lines the airway wall. We study the instability of an axisymmetric layer of ...viscoplastic Bingham liquid coating the interior of a rigid tube, which is a simple model for an airway that takes into account the yield stress of mucus. An evolution equation for the thickness of the liquid layer is derived using long-wave theory, from which we also derive a simpler thin-film evolution equation. In the thin-film case we show that two branches of marginally yielded static solutions of the evolution equation can be used to both predict the size of the initial perturbation required to trigger instability and quantify how increasing the capillary Bingham number (a parameter measuring yield stress relative to surface tension) reduces the final deformation of the layer. Using numerical solutions of the long-wave evolution equation, we quantify how the critical layer thickness required to form a liquid plug in the tube increases as the capillary Bingham number is increased. We discuss the significance of these findings for modelling airway closure in obstructive conditions such as cystic fibrosis, where the mucus layer is often thicker and has a higher yield stress.
Cotton (Gossypium spp.) is the most important natural fiber crop worldwide. The diversity of Gossypium species also provides an ideal model for investigating evolution and domestication of ...polyploids. However, the huge and complex cotton genome hinders genomic research. Technical advances in high-throughput sequencing and bioinformatics analysis have now largely overcome these obstacles, bringing about a new era of cotton genomics. Here, we review recent progress in Gossypium genomics based on whole genome sequencing, resequencing, and comparative genomics, which have provided insights about the genomic basis of fiber biogenesis and the landscape of cotton functional genomics. We address current challenges and present multidisciplinary genomics-enabled breeding strategies covering the breadth of high fiber yield, quality, and environmental resilience for future cotton breeding programs.
Cotton is an important natural fiber crop cultivated worldwide that also provides an ideal model for investigating evolution and domestication of polyploidsCombinations of the latest technologies, such as optical mapping, high-throughput chromosome conformation capture (Hi-C), and Pacific Biosciences (PacBio) long-reads, have been used to generate multiple high-quality reference genomes of diploid and allotetraploid cotton.Comparative population genomics illuminated the genetic history of cotton domestication and identified the genomic variation determining fiber yield, quality, and stress resistance.
Due to the high specific stiffness and low coefficient of thermal expansion, the SiC particles are introduced to improve the dimensional stability of Al-Cu-Mg (2024 aluminum) alloy in present work. ...The intergrated effects of SiC and aging precipitates on dimensional stability of 2024 aluminum alloy are investigated during the two-stage aging process. It is observed that, with increasing the addition of SiC, the amount of S′ precipitates decreases, and the micro-yield strength first increases and then decreases, while the thermal strain (εc) first decreases and then increases. When the addition of SiC was 10 wt%, both the micro-yield strength and the thermal strain reach the optimal value. It is attributed to the effect of SiC and aging precipitates on pinning the dislocations, which would consume more stored elastic energy, thus leading to an increase in the dimensional stability of the alloy.
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•SiC addition is introduced to increase the dimensional stability of AA2024.•The dimensional stability can be enhanced by the intergrated effect of SiC and S’.•Pinning dislocation and eliminating elastic energy improve dimensional stability.•The optimal addition of SiC in the AA2024 is 10 wt%.
•Sulphur-coated and polymer-coated urea had intensive N release during tillering and ineffective stage.•Deep placement with controlled-released nitrogen fertilizers increased N ...leaching.•Sulphur-coated urea had the highest N leaching under both fertilization placements.•Bulk blending mixture fertilizer exhibited the lowest N leaching under both fertilization placements.
In the Taihu region of China, overuse of chemical nitrogen (N) fertilizer is often associated with low nitrogen recovery (NRE) and leads to serious groundwater pollution caused by N leaching losses. Controlled-released nitrogen fertilizers (CRNFs) and mechanized deep placement are promising alternatives to broadcasting urea to increase crop yield and NRE in machine-transplanted rice production. However, their interactions with regard to soil N status, N leaching and crop performance are unclear. A two-year (2015 and 2016) field experiment was conducted in a randomized complete block using two fertilization techniques (broadcast and deep placement by mechanical side-dressing fertilization) and three CRNFs (sulphur-coated urea (SCU), polymer-coated urea (PCU) and a bulk blended mixture (BBF)). Conventional high-yield fertilization (four split applications of urea at 216 kg N ha−1 (CK)) and 0–N treatments were established as controls. The results showed that the variation in NH4+-N concentration in the percolation and surface water varied across the different CRNFs, irrespective of the techniques used. NO3− -N concentration in the percolation water varied with water conditions in the field. Deep placement with CRNF correspondingly increased mineral N concentration in percolation at depths of 20 and 60 cm but reduced it in the surface water compared to that of the broadcast, although the benefits varied depending on the CRNF type and growth stage. Deep placement of SCU and PCU significantly increased N leaching and the mineral N in the 40–60 cm soil layer compared to that of the broadcast, due to the intensive N release during tillering and ineffective stage when the rice plant had a weak N uptake capability. Deep placement of SCU had the highest N leaching of 6.65 and 5.34 kg N ha−1 during 2015 and 2016. In contrast, BBF exhibited the lowest N leaching, regardless of fertilization placement, which apparently synchronized N release rates with rice N uptake patterns. In the present study, BBF obtained higher rice yields and N recoveries, without significantly enhancing mineral N leaching losses, when compared to CK. Our results suggest that the use of BBF is a promising alternative to a conventional high-yield fertilization practice, especially if combined with deep placement.
•LASSO greatly outperformed SFS in computation time.•Sequential Forward Selection (SFS) and LASSO gave equally good yield predictions.•NDVI showed good prediction ability of grain yield in final ...grain filling stages.•MTC and EVI are more important grain yield predictors during early grain filling.•Best predictions resulted from combining vegetation indices from multitemporal data.
Traditional plant breeding based on selection for grain yield is time-consuming and costly; therefore, new innovative methods are in high demand to reduce costs and accelerate genetic gains. Remote sensing-based platforms such as unmanned aerial vehicles (UAV) show promise to predict different traits including grain yield. Attention is currently being devoted to machine learning methods in order to extract the most meaningful information from the massive amounts of data generated by UAV images. These methods have shown a promising capability to come up with nonlinearity and explore patterns beyond the human ability. This study investigates the application of two different machine learning based regressor methods to predict wheat grain yield using extracted vegetation indices from UAV images. The goal of the study was to investigate the strength of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for grain yield prediction and compare the results with LASSO regressor with an internal feature selector. Models were tested on grain yield data from 600 plots of spring wheat planted in South-Eastern Norway in 2018. Five spectral bands along with three different vegetation indices; the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and MERIS Terrestrial Chlorophyll Index (MTCI) were extracted from multispectral images at three dates between heading and maturity of the plants. These features for each field trial plot at each date were used as input data for the SVR model. The best model hyperparameters were estimated using grid search. Based on feature selection results from both methods, NDVI showed the highest prediction ability for grain yield at all dates and its explanatory power increased toward maturity, while adding MTCI and EVI at earlier stages of grain filling improved model performance. Combined models based on all indices and dates explained up to 90% of the variation in grain yield on the test set. Inclusion of individual bands added collinearity to the models and did not improve the predictions. Although both regression methods showed a good capability for grain yield prediction, LASSO regressor proved to be more affordable and economical in terms of time.
Many natural phenomena in geophysics and hydrogeology involve the flow of non‐Newtonian fluids through natural rough‐walled fractures. Therefore, there is considerable interest in predicting the ...pressure drop generated by complex flow in these media under a given set of boundary conditions. However, this task is markedly more challenging than the Newtonian case given the coupling of geometrical and rheological parameters in the flow law. The main contribution of this paper is to propose a simple method to predict the flow of commonly used Carreau and yield stress fluids through fractures. To do so, an expression relating the “in situ” shear viscosity of the fluid to the bulk shear‐viscosity parameters is obtained. Then, this “in situ” viscosity is entered in the macroscopic laws to predict the flow rate‐pressure gradient relations. Experiments with yield stress and Carreau fluids in two replicas of natural fractures covering a wide range of injection flow rates are presented and compared to the predictions of the proposed method. Our results show that the use of a constant shift parameter to relate “in situ” and bulk shear viscosity is no longer valid in the presence of a yield stress or a plateau viscosity. Consequently, properly representing the dependence of the shift parameter on the flow rate is crucial to obtain accurate predictions. The proposed method predicts the pressure drop in a rough‐walled fracture at a given injection flow rate by only using the shear rheology of the fluid, the hydraulic aperture of the fracture, and the inertial coefficients as inputs.
Key Points
A simple method to predict the flow of commonly used Carreau and yield stress fluids through fractures is proposed
A set of experiments was performed to assess the accuracy of the proposed method, finding good agreement between predictions and experiments
The use of a constant shift parameter to relate “in situ” and bulk shear viscosity is not valid for Carreau and yield stress fluids
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