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

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

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

Strategic tillage for the improvement of no-till farming systems

Farming with no or zero tillage (NT) is a valuable practice in many agro-ecosystems, but problems may develop that can be solved, or NT may otherwise be improved, by occasional strategic tillage (ST). The practice of ST has been evaluated in numerous studies. Problems addressed by ST in research have included weed control, soil compaction, water infiltration, SOC sequestration, vertical stratification of soil properties, and runoff of soluble nutrients. Very often ST has had no or small short-term positive and negative effects. Increases have occurred more frequently than decreases with ST for water infiltration, erosion, P availability, and grain yield. Decreases have occurred more frequently than increases with ST for dissolved nutrient loss, weed numbers, microbial biomass or activity, bulk density of the surface soil, and soil compaction. Benefits with no associated detrimental effects were more likely to occur with deep inversion tillage compared with shallow tillage. Successful ST requires careful consideration of the production situation and the problem to be solved and then making a good choice of the tillage type, depth, timing, and frequency

Impacts of strategic tillage on soil erosion, nutrient loss in runoff and nitrous oxide emission

Strategic (once-off) tillage (ST) of continuous long-term no-tillage (NT) soil may help to control weeds prior to establishing the following crop but may also increase the risk of erosion and nutrient loss in runoff and greenhouse gas emission compared with NT soil. The objective of this study was to measure these short-term runoff and gaseous emission impacts after ST of NT, controlled traffic farming soils. A rainfall simulator was used to generate runoff from four plots (2.75 m length × 0.75 m width) each of NT and ST on a Vertosol, Dermosol and Sodosol. Runoff was generated from heavy rainfall (70 mm h-) and samples analyzed for volume, sediment and nutrient contents. Short-term nitrous oxide (N2O) emissions were measured from the Vertosol and Sodosol before and after rainfall using the passive chamber technique. On the Dermosol and Sodosol there was more runoff from ST plots than from NT plots (P0.05). Erosion was highest after ST on the Sodosol (8.3 tha- suspended sediment) and there were no treatment differences on the other soils. Total nitrogen (N) loads in runoff followed a similar pattern. Dissolved phosphorus (P) concentrations and total P loads were higher (P0.05) Over the measuring period, cumulative N2O-N emissions from the Vertosol and Sodosol were approximately 1 and 0.6 gha, respectively. Strategic tillage for weed control increased the susceptibility of Sodosols and Dermosols to water, sediment and nutrient losses in runoff after heavy rainfall but had minimal impact on N2O emissions

Genetic diversity in barley and wheat for tolerance to soil constraints

Surface soil sodicity as well as subsoil salinity, acidity, and phytotoxic concentrations o chloride (Cl) are major soil constraints to crop production in many soils of sub-Tropical, north-easter Australia. The identification of genotypes tolerant to these soil constraints may be an optio to maintain and improve productivity on these soils. We evaluated performance of 11 barle and 17 wheat genotypes grown on two sites 800 mgClkg1) in the subsoil (0.9-1.3 m soi depth) and higher exchangeable sodium percentage (ESP) (>6%) in the surface and subsoil. Barle grain yield and plant available water capacity (PAWC) were reduced between 5%-25% and 40%-66% respectively, for different genotypes at the sodic site as compared to the non-sodic site. For whea genotypes, grain yield was between 8% and 33% lower at the sodic site compared to the non-sodi site and PAWC was between 3% and 37% lower. Most barley and wheat genotypes grown at the sodi site showed calcium (Ca) deficiency symptoms on younger leaves. Analysis of the youngest full mature leaf (YML) confirmed that genotypes grown at the sodic site with Ca concentration < 0.2 exhibited deficiency symptoms. Grain yields of both barley and wheat genotypes grown on th sodic and non-sodic sites increased significantly with increasing Ca and K in YML and decrease significantly with increasing Na and Cl concentrations in YML. Sodium (Na) concentrations in YM of wheat genotypes grown at the sodic site were 10-fold higher than those from the non-sodic sit whereas this increase was only two-fold in barley genotypes. In step-wise regression, the PAWC o barley and wheat genotypes grown on sodic and non-sodic sites was the principal determinant o variability of barley and wheat grain yield. Including the Ca concentration in the YML of whea genotypes and K:Na ratio in the YML of barley genotypes significantly improved the predictio of grain yield in the regression analysis. Barley genotypes, Mackay and Kaputar, were relativel susceptible while Baronesse and Grout were relatively more tolerant to sodicity. Wheat genotype Gregory and Stampede were generally relatively more susceptible to sodicity, and genotypes Baxter Hume, and the experimental line HSF1-255 were relatively more tolerant than the former group

The ability of conservation agriculture to conserve soil organic carbon and the subsequent impact on soil physical, chemical, and biological properties and yield

Increases in human populations and the emerging challenges of climate change mean that the world's agricultural systems will need to produce more food in an environment that is increasingly variable and where the quality of our natural resource base is declining. One central measure of an agricultural system's capacity to do this is its ability to preserve soil organic carbon (SOC), due to the pivotal role that this plays in maintaining soil physical, chemical, and biological properties and ultimately yield. This narrative review examines the literature published worldwide over the last 30 years to assess the impact of one widely applied agricultural management system, conservation agriculture (CA), on its ability to maintain SOC and the subsequent impacts on soil physical, chemical and biological properties, and yield. While the effects of CA on SOC worldwide are variable, with both increases and decreases observed, in regions where soil and climatic conditions are favorable for biomass production and where the system does not negatively impact yield, then CA can lead to higher amounts of SOC relative to conventionally managed systems, particularly in the surface of the soil profile. Where greater SOC occurs, these are also often accompanied by improvements in soil structure, water infiltration and soil water storage, plant nutrient availability, microbial biomass and diversity, and yield. However, where CA is used in certain environments (e.g., cold, wet environments with poorly drained soils) or where the CA system has not been well-adapted to local conditions, taking into account the specific agronomic, social, and environmental challenges present, then it may not be a successful system of management. Farmers require access to a range of tools and resources to allow them to identify if the principles of CA are likely to lead be appropriate for their situation and well-designed, locally adapted systems to successfully overcome the agronomic, social and economic challenges that can be associated with its use

Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models

One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions