Costs of edaphic stress to Australian grains industry

We determined the costs of soil constraints to Australian grains industry

Does strategic tillage undo long-term improvement in soils under no-till?

We determine pros and cons of strategic tillage in continuous no tillage

D'Annunzio sulla scena lirica: libretto o "Poema"?

Australia Direct Action climate change policy relies on purchasing greenhouse gas abatement from projects undertaking approved abatement activities. Management of soil organic carbon (SOC) in agricultural soils is an approved activity, based on the expectation that land use change can deliver significant changes in SOC. However, there are concerns that climate, topography and soil texture will limit changes in SOC stocks. This work analyses data from 1482 sites surveyed across the major agricultural regions of Eastern Australia to determine the relative importance of land use vs. other drivers of SOC. Variation in land use explained only 1.4% of the total variation in SOC, with aridity and soil texture the main regulators of SOC stock under different land uses. Results suggest the greatest potential for increasing SOC stocks in Eastern Australian agricultural regions lies in converting from cropping to pasture on heavy textured soils in the humid regions

From soil health to agricultural productivity: The critical role of soil constraint management

Soil constraints significantly impact agricultural productivity and food security by affecting soil health and crop yields. This study provides a comprehensive global bibliometric analysis of global research on soil physical, chemical and biological constraints, utilizing R, VOSviewer, and Citespace. Global publications totaled 1,418 showing a significant increase in output since the early 2000 s, with Australia and the United States leading in research contributions. The top journals accounted for 13.13% of the total publications, with major contributions from institutions in Australia, the United States and China. Key research themes identified include the impact of climate change, nutrient management, and crop-specific responses to soil constraints. Moreover, the analysis showed a shift towards advanced scientific techniques and technologies in recent years, such as molecular biology, proteomics, and remote sensing, which reflects the evolving focus of soil constraint research. The studies in the 2000 s primarily focused on traditional soil management practices and the identification of basic nutrient deficiencies. However, the recent shift towards advanced methodologies highlights an evolving focus on precise, high-resolution techniques for understanding and mitigating soil constraints. Despite these advancements, potential gaps remain in the integration of these technologies into practical soil management strategies, and in addressing regional differences in soil constraints. Our study emphasizes the importance of continued international collaboration and the integration of innovative methodologies to address the complex challenges of soil management. The future research should further support the realization of the global Sustainable Development Goals (SDGs) by adopting scientific soil management measures, applying appropriate fertilizers, improving soil structure, reducing soil pollution and erosion, and enhancing agricultural sustainability and food security

Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to assist crop phenotyping on sodic soil are limited. Hence, this study evaluated the ability of multispectral, hyperspectral, 3D point cloud, and machine learning techniques to improve estimation of biomass and grain yield of wheat genotypes grown on a moderately sodic (MS)and highly sodic (HS) soil sites in northeastern Australia. While a number of studies have reported using different remote sensing approaches and crop traits to quantify crop growth, stress, and yield variation, studies are limited using the combination of these techniques including machine learning to improve estimation of genotypic biomass and yield, especially in constrained sodic soil environments. At close to flowering, unmanned aerial vehicle (UAV) and ground-based proximal sensing was used to obtain remote and/or proximal sensing data, while biomass yield and crop heights were also manually measured in the field. Grain yield was machine-harvested at maturity. UAV remote and/or proximal sensing-derived spectral vegetation indices (VIs), such as normalized difference vegetation index, optimized soil adjusted vegetation index, and enhanced vegetation index and crop height were closely corresponded to wheat genotypic biomass and grain yields. UAV multispectral VIs more closely associated with biomass and grain yields compared to proximal sensing data. The red-green- blue (RGB) 3D point cloud technique was effective in determining crop height, which was slightly better correlated with genotypic biomass and grain yield than ground-measured crop height data. These remote sensing-derived crop traits (VIs and crop height) and wheat biomass and grain yields were further simulated using machine learning algorithms (multitarget linear regression, support vector machine regression, Gaussian process regression, and artificial neural network) with different kernels to improve estimation of biomass and grain yield. The artificial neural network predicted biomass yield (R2 = 0.89; RMSE = 34.8 g/m2 for the MS and R2 = 0.82; RMSE = 26.4 g/m2 for the HS site) and grain yield (R2 = 0.88; RMSE = 11.8 g/m2 for the MS and R2 = 0.74; RMSE = 16.1 g/m2 for the HS site) with slightly less error than the others. Wheat genotypes Mitch, Corack, Mace, Trojan, Lancer, and Bremer were identified as more tolerant to sodic soil constraints than Emu Rock, Janz, Flanker, and Gladius. The study improves our ability to select appropriate traits and techniques in accurate estimation of wheat genotypic biomass and grain yields on sodic soils. This will also assist farmers in identifying cultivars tolerant to sodic soil constraints

Quantifying the economic impact of soil constraints on Australian agriculture: a case study of wheat

Soil sodicity, acidity, and salinity are important soil constraints to wheat production in many cropping regions across Australia, and the Australian agricultural industry needs accurate information on their economic impacts to guide investment decisions on remediation and minimize productivity losses. We present a modelling framework that maps the effects of soil constraints on wheat yield, quantifying forfeited wheat yields due to specific soil constraints at a broad spatial scale and assessing the economic benefit of managing these constraints. Of the three soil constraints considered (sodicity, acidity, and salinity), sodicity caused the largest magnitude of yield gaps across most of the wheat-cropping areas of Australia, with an average yield gap of 0.13\ua0t hayr. Yield gaps due to acidity were more concentrated spatially in the high-rainfall regions of Western Australia, Victoria, and New South Wales, and averaged 0.04\ua0t hayr across the wheat-cropping areas of Australia, whereas the yield gap due to salinity was estimated to be 0.02\ua0t hayr. The lost opportunity associated with soil sodicity for wheat production was estimated to be worth A$1,300 million per annum, for soil acidity, A$400 million per annum, and for salinity, A$200 million per annum. The results of this work should prove useful to guide national investment decisions on the allocation of resources and to target areas where more detailed information would be required in order to reduce the yield gap associated with soil constraints on wheat yields in Australia

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke