Ellinbank Observatory

• ML predicts tillage and stubble practices from 22 years of soil data. • Random forest outperforms spatial baseline in predicting farm practices. • Year and latitude are key drivers of no-till and stubble grazing adoption. • Gridded maps reveal national trends in tillage and residue management. • Framework supports GHG accounting modelling and soil health policy development. Tillage and stubble management are key drivers of soil health, crop productivity, and greenhouse gas (GHG) emissions, yet long-term spatially explicit data on these practices remain scarce. Using data from two Australian national soil monitoring programs Soil Carbon Research Project (SCaRP) and Soil Organic Carbon Monitoring (SOC-M) we analyzed over two decades (2001–2023) of tillage and stubble management records collated across 300 Australian farmer paddock scale datasets. We developed machine learning models based on random forest to predict spatial and temporal trends in these practices, incorporating crop type, soil classification, climate variables, and spatial coordinates as predictors. Our models were benchmarked against multinomial logistic regression and a nearest-neighbour baseline, demonstrating that random forest consistently achieved higher accuracy, particularly for dominant practices such as no-tillage and stubble grazing. Variable importance analyses identified “Year” as the most influential predictors, capturing widespread no-tillage adoption post-2010, while latitude and crop type further explained regional variations. Partial dependence plots revealed sharp inflection points in adoption trajectories around 2010, coinciding with major policy and technological shifts. We applied the models to generate gridded predictions (0.5° resolution) across the Australian cropping zone, providing insights into the evolving distribution of tillage and stubble practices. These spatial products not only improve GHG accounting frameworks and Earth system models but also offer actionable intelligence for soil health policy and targeted extension programs. Our approach demonstrates how combining ground-based monitoring with machine learning can fill critical data gaps and provide a scalable framework for agricultural management assessment and climate mitigation strategies.

Abstract We present the discovery of 30 transiting giant planets that were initially detected using data from NASA’s Transiting Exoplanet Survey Satellite mission. These new planets orbit relatively bright ( G ≤ 12.5) FGK host stars with orbital periods between 1.6 and 8.2 days, and have radii between 0.9 and 1.7 Jupiter radii. We performed follow-up ground-based photometry, high angular resolution imaging, high-resolution spectroscopy, and radial velocity monitoring for each of these objects to confirm that they are planets and determine their masses and other system parameters. The planets’ masses span more than an order of magnitude (0.17 M J &lt; M p &lt; 3.3 M J ). For two planets, TOI-3593 b and TOI-4961 b, we measured significant nonzero eccentricities of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0.1</mml:mn> <mml:msubsup> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>0.03</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>0.05</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0.1</mml:mn> <mml:msubsup> <mml:mrow> <mml:mn>8</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>0.05</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>0.04</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> , respectively, while for the other planets, the data typically provide a 1 σ upper bound of 0.15 on the eccentricity. These discoveries represent a major step toward assembling a complete, magnitude-limited sample of transiting hot Jupiters around FGK stars.

Genomic Prediction (GP) considering Genotype by Environment (G×E) interactions was, for the first time, used to assess the environment-specific seasonal performance and genetic potential of perennial ryegrass (Lolium perenne L.) in a regional evaluation system across southeastern Australia. The study analysed the Dry Matter Yield (DMY) of 72 base cultivars and endophyte symbiotic effects using multi-harvest, multi-site trial data, and genomic data in a best linear unbiased prediction framework. Spatial analysis corrected for field heterogeneities, while Leave-One-Out Cross Validation assessed predictive ability. Results identified two distinct mega-environments: mainland Australia (AUM) and Tasmania (TAS), with cultivars showing environment-specific adaptation (Base and Bealey in AUM; Platinum and Avalon in TAS) or broad adaptability (Shogun). The G×E-enhanced GP model demonstrated an overall 24.9% improved predictive accuracy (Lin's Concordance Correlation Coefficient, CCC: 0.542) over the Australian industry-standard best linear unbiased estimation model (CCC: 0.434), with genomic information contributing a 12.7% improvement (CCC: from 0.434 to 0.489) and G×E modelling providing an additional 10.8% increase (CCC: from 0.489 to 0.542). Narrow-sense heritability increased from 0.31 to 0.39 with G×E inclusion, while broad-sense heritability remained high in both mega-environments (AUM: 0.73, TAS: 0.74). These findings support informed cultivar selection for the Australian dairy industry and enable genomics-based parental selection in future breeding programs.

Abstract Sub-Neptunes—volatile-rich exoplanets smaller than Neptune—are intrinsically the most common type of planet known. However, the formation and nature of these objects, as well as the distinctions between subclasses (if any), remain unclear. Two powerful tools to tease out the secrets of these worlds are measurements of (i) atmospheric composition and structure revealed by transit and/or eclipse spectroscopy, and (ii) mass, radius, and density revealed by transit photometry and Doppler spectroscopy. Here, we present OrCAS, a survey to better elucidate the origins, compositions, and atmospheres of sub-Neptunes. This radial velocity survey uses a repeatable, quantifiable metric to select targets suitable for subsequent transmission spectroscopy and address key science themes about the atmospheric and internal compositions and architectures of these systems. Our survey targets 26 systems with transiting sub-Neptune planet candidates, with the overarching goal of increasing the sample of such planets suitable for subsequent atmospheric characterization. This paper lays out our survey's science goals, defines our target prioritization metric, and performs light-curve fits and statistical validation using existing TESS photometry and ground-based follow-up observations. Our survey serves to continue expanding the sample of small exoplanets with well-measured properties orbiting nearby bright stars, ensuring fruitful studies of these systems for many years to come.

Pasture persistence is the ability to maintain plant density and dry matter production of sown species throughout the life of a sward, and it is important for the long-term productivity and sustainability of pasture-based animal production systems. Identifying early indicators of declining pasture persistence enables livestock farmers to implement timely management strategies to use their land more productively and sustainably. However, there are significant gaps in current knowledge in which early indicators of pasture decline should be monitored, when, and at what scale. Traditionally, persistence assessment rely on manual pasture measurements, which are either subjective and labour-intensive or lack timeliness for decision making and are unlikely to allow livestock producers to identify the symptoms of decline in sown pasture before it becomes a significant issue. With the rapid development of sensors and image processing algorithms, remote sensing platforms show promise in reducing the time frame for phenotyping early indicators of declining pasture persistence. This review discussed which dynamic morphological, and physiological traits, along with biological processes, could be considered reliable early indicators of persistence risk in sown pastures, as well as risk factors that are likely to put a sward at a disadvantage with regards to longevity, and how high-throughput phenotyping (HTP) can measure these indicators and risk factors. This study addressed the knowledge gap on monitoring early indicators of declining pasture persistence using remote sensing technologies, and may provide valuable insights that could be used to establish an early warning system for persistence risk.

The addition of red seaweeds from the genus Asparagopsis to the diet of dairy cattle has emerged as a promising methane mitigation strategy. Research to date characterizing the effect of feeding Asparagopsis derived formulations on milk processing properties for manufacture of products such as cheese and yoghurt has been limited. With the potential for adverse changes to milk properties intrinsically connected to the cows’ diet it is important to elucidate the effect of dietary methane mitigation products on milk properties. The aim of this research was to evaluate the effect of feeding two formulations of Asparagopsis armata steeped in canola oil (ASP-Oil) to lactating dairy cows on compositional and functional properties of milk. Thirty-nine lactating, multiparous, Holstein-Friesian cows were randomly allocated one of three supplement treatments that was added to a similar basal diet: control of canola oil (CON), ASP-Oil with the seaweed biomass removed (ASP1), and ASP-Oil with the seaweed biomass retained (ASP2) over a 32-day experiment. Treatments were given twice daily, using a pulse feeding technique, with the oils combined with a grain concentrate during each milking. The basal diet consisted of vetch hay ad libitum and 7.0 kg DM/d of the grain mix. Results demonstrated there was no difference in milk headspace volatiles measured in whole milk samples, or the rennet-induced and acid-induced gelation properties of skimmed milk from cows fed the CON or ASP-Oil treatment diets. Moreover, differences in the diet had only a minor effect on milk fatty acid profile. These results indicate that methane mitigation protocols that involve the introduction of A. armata to dairy cow diets can be utilized without negative effects on milk fatty acid profile, processability, or function.

We report the discovery of a hot Jupiter on a 3.28-day orbit around a 1.08 M⊙ G0 star that is the secondary component in a loose binary system. Based on follow-up radial velocity observations of TOI-858 B with CORALIE on the Swiss 1.2 m telescope and CHIRON on the 1.5 m telescope at the Cerro Tololo Inter-American Observatory (CTIO), we measured the planet mass to be 1.10−0.07+0.08 MJ. Two transits were further observed with CORALIE to determine the alignment of TOI-858 B b with respect to its host star. Analysis of the Rossiter-McLaughlin signal from the planet shows that the sky-projected obliquity is λ = 99.3−3.7+3.8°. Numerical simulations show that the neighbour star TOI-858 A is too distant to have trapped the planet in a Kozai–Lidov resonance, suggesting a different dynamical evolution or a primordial origin to explain this misalignment. The 1.15 M⊙ primary F9 star of the system (TYC 8501-01597-1, at ρ ~11″) was also observed with CORALIE in order to provide upper limits for the presence of aplanetary companion orbiting that star.

In this single cohort study, we investigated associations between the concentrations of a suite of serum biomarkers measured in the first 30 d of lactation and subsequent reproductive performance measured as mating start date to conception intervals, in pasture-based Holstein cows. A secondary objective was to examine associations between biomarker concentrations and 305-d milk yield to assess whether any positive associations between biomarker concentration and reproductive performance were explained by reduced milk production. The data used had been collected as part of an ongoing project from 2017 to 2020 to compile a data set from a large population of lactating dairy cows. Biomarkers measured were those associated with energy balance (β-hydroxybutyrate [BHB] and nonesterified fatty acids [NEFA]), protein nutritional status (urea and albumin), immune status (globulin, albumin to globulin ratio and haptoglobin), and macromineral status (calcium and magnesium). Associations between biomarker concentrations and mating start date to conception interval were investigated using Cox proportional hazard models, using between 634 and 1,121 lactations (varying by biomarker) from 632 to 1,103 cows and 11 to 17 mating periods from 10 to 13 herds. Based on hazard ratio (HR) estimates and associated 95% confidence intervals (CI), hazard of conception on any particular day of the herds' mating periods was positively associated with the concentrations of albumin (HR = 1.09; 95% CI: 1.05-1.12), albumin to globulin ratio (HR = 2.82; 95% CI: 1.66-4.79), calcium (HR = 2.01; 95% CI: 1.18-3.43), and magnesium (HR = 2.17; 95% CI: 1.01-4.66), and negatively associated with globulin concentration (HR = 0.98; 95% CI: 0.97 to 1.00). There was also some evidence that NEFA concentration was negatively associated (HR = 0.76; 95% CI: 0.57 to 1.01), and urea concentration positively associated (HR = 1.05; 95% CI: 0.99 to 1.11), with reproductive performance, but no evidence that BHB and haptoglobin concentrations were associated with reproductive performance. Except for NEFA, presence and direction of the associations between the biomarker and milk yield were not discordant with that for reproductive performance. Also, except for NEFA, we found no substantial evidence of nonlinear relationships between biomarker concentration and either reproductive performance or milk yield. Correlations between biomarker concentrations were generally weak, indicating that multibiomarker panels may collectively predict reproductive performance better than any single biomarker. We noted substantial variation in the concentrations of all biomarkers within, and for some biomarkers, between herd-year groups. Collectively, these results indicate that there may be scope to improve biomarker concentrations through nutritional, management, and genetic interventions, and by association, reproductive performance and milk yield may also improve.

Perennial ryegrass (Lolium perenne L.) is a commonly grown pasture species in temperate agriculture, mainly serving as a primary energy source for dairy cows. However, its limited persistence often leads to missed production potential and early resowing, especially in countries that experience summer drought, e.g., Australia and New Zealand. Therefore, understanding the factors influencing perennial ryegrass pasture persistence is crucial for sustainable land management and climate resilience in pasture-based animal production systems. Significant gaps in knowledge exist regarding the factors influencing pasture persistence, as the number of conducted studies in this area remains limited. This study aimed to investigate the factors influencing the expression of persistence in perennial ryegrass populations using airborne and ground-based sensors. A field experiment was conducted in the southwest region of Victoria, Australia, involving ten commercial perennial ryegrass cultivar–endophyte combinations in two different populations. Persistence was evaluated using sensor-based and conventional pasture measurements over two consecutive autumns. The results revealed significant fixed effects of cultivar, endophyte, and environment and their interactions on persistence traits of perennial ryegrass. Cultivars Alto, Samson, and One50 exhibited high levels of persistence when infected with novel endophyte strains. Furthermore, prolonged environmental stresses were found to drive directional selection within pasture populations. The findings emphasise the importance of selecting appropriate cultivar–endophyte combinations and early detection of signs of poor persistence to optimise sward longevity and financial returns from pasture-based animal production systems. This study fills a knowledge gap regarding the factors influencing pasture persistence and provides valuable insights for sustainable pasture management strategies.