Context. Salt stress is one of the major, ever-increasing abiotic stresses that hinders rice production across arable land around the world. In order to sustain the production of rice (Oryza sativa) in these salt-affected areas, high-yielding stable salt tolerant genotypes must be identified.

Aims. The additive main effects and multiplicative interaction (AMMI) model was carried out to identify high-yielding stable rice genotypes under both saline and alkali stress.

Methods. Nineteen promising rice genotypes including five standard checks were evaluated using randomized block design under nine salt stress environments using three replications in 2017 and 2018.

Key results. The AMMI model II is thought to be the best model for genotype identification based on prediction accuracy with high GEI_S and low GEI_N (genotype and environment interaction noise). According to AMMI model II, six genotypes were identified as the top performers under salt stress: one genotype (CSR RIL-01-IR 165) yielded the best in three environments; another genotype (CSR 2711-17) yielded highly in in two environments; and the remaining three genotypes (RP5989-2-4-8-15-139-62-6-9, RP 6188-GSR IR1-8-S6-S3-S1, RP6189-HHZ17-Y16-Y3-SAL1) as well as one control genotype (CHK2) yielded well in single environments.

Conclusions. Based on AMMI stability study, genotypes RP5989-2-4-8-15-139-62-6-9, CSR2711-17, CSR RIL-01-IR 165, CSR-2748-4441-195, CSR-2748-4441-193), and CSRRIL-01-IR 75 were determined to be higher yielding and more stable than the national control genotype (CSR23).

Implications. The high-yielding stable genotypes identified in this study could be planted for salt-affected areas to sustain the production of rice.

Context. Omega-3 in pasture-fed beef and lamb is related to its availability in the forage species grazed. The variation in omega-3 content of several forages has been examined in the USA and UK, but not in south-eastern Australia across different stages of maturity.

Aims. This study aimed to determine the change in omega-3, at different stages of plant development, in four species of forage commonly grazed by ruminants in south-eastern Australia.

Methods. Four species, oats (Avena sativa L. cv. Eurabbie), annual ryegrass (Lolium perenne L. cv. Rambo), phalaris (Phalaris aquatica L. cv HoldfastGT) and subterranean clover (Trifolium subterrraneum L. cv. Coolamon) were grown in a replicated pot trial and harvested at seven stages of maturity corresponding to early vegetative, late vegetative, stem elongation, boot, anthesis, soft dough and ripening for oats, phalaris and ryegrass, or relative to day of flowering in clover. Proximate analysis and the concentration of fatty acids including C18:3n-3 and C18:2n-6 as well as the ratio of n-6:n-3 was determined.

Key results. The mean (±s.e.) proportion of C18:3n-3 was highest in clover (50.6 ± 0.8), and higher in ryegrass (45.1 ± 0.5) and phalaris (44.0 ± 0.6) compared with oats (39.4 ± 0.5). Omega-3 proportion decreased with increasing maturity for oats (13.9 ± 0.86 vs 64.3 ± 0.68), ryegrass (17.3 ± 0.86 vs 68.2 ± 0.65) and phalaris (21.6 ± 1.63 vs 65.1 ± 0.79) but not to the same extent for sub clover (41.5 ± 1.38 vs 57.4 ± 0.79). The proportion of omega-3 was positively correlated with crude protein content.

Conclusions. Omega-3 levels decreased in forages during development and was positively related to crude protein content, which is likely associated with total leaf chloroplasts. Although crude protein content remained higher for clover compared with other species, this did not translate to a higher proportion of omega-3 for any crude protein level.

Implications. Grazing forages at earlier stages of maturity and maximising the amount of crude protein available for growth will increase the amount of omega-3 in plant material. The availability of this omega-3 for incorporation into meat and milk should be examined.

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.

Context. The grain-growing areas of north-eastern Australia are a major producer of grain for human and livestock consumption, but declining soil nitrogen (N) and phosphorus (P) fertility is increasing fertiliser requirements to sustain productivity. Adding a concentrated zone of fertiliser P to the subsoil (i.e. a ‘deep P’ band) is an effective strategy to increase plant P uptake in farming systems reliant on stored soil water. However, crop responses to deep P with contrasting soil N status remain unclear.

Aim. This study aimed to assess responses of sorghum (Sorghum bicolor) to fertiliser P with contrasting distributions of soil N.

Method. A lysimeter experiment was conducted in semi-controlled environment, where sorghum was grown to physiological maturity in P depleted Vertosol with contrasting fertiliser N and P additions.

Key findings. Responses of sorghum to deep P were optimised when bands were placed in N enriched soil in the 0–20 cm layer, producing comparable biomass to when P was dispersed throughout the soil volume. Localised root proliferation around the deep P band was maximised when bands were placed into N-enriched soil, however plant P uptake was only 77% of that with dispersed P.

Conclusions. Sorghum responses to deep P were affected by the distribution of soil N within the top 60 cm of the soil profile, with maximum dry matter production, N and P uptake occurring when high concentrations of N and P were co-located in the 0–20 cm layer.

Implications. Adequate N status of the upper soil profile is required to optimise sorghum responses to deep P.

Context. Current decision support systems (DSS) for phosphorus (P) fertiliser were developed using data from historical cropping systems. An understanding of how soil properties and rainfall influence wheat (Triticum aestivum) response to P fertiliser in current systems is required to optimise P management.

Aims. The aims of this study were to: (1) assess the soil properties that have the greatest influence on relative yield; (2) examine how rainfall conditions influence relative yield; and (3) examine whether there were interactive effects between rainfall and soil properties on relative yield.

Methods. Forty P rate-response field experiments were completed in Western Australia. Regression tree modelling, soil test calibration curves and the sliding window approach were used to examine relationships between soil properties or rainfall and relative yield.

Key results. Phosphorus buffering index (PBI) was important for determining the factors that influence relative yield. For sites with PBI 0–10 cm <56 (n = 30), regression tree modelling showed rainfall before sowing and soil pH_Ca were important factors (R² = 0.59). For sites where PBI >56 (n = 10), relative yield was closely related to plant-available P at 0–10 cm and the r-value for the calibration curve was 0.95.

Conclusions. Rainfall and soil pH_Ca influence wheat response to P where PBI <56 is attributed to an accumulation of soil P after decades of fertiliser applications and the availability of stored soil P to crops.

Implications. Pre-sowing rainfall should be included in DSS so that grain producers can make informed, tactical decisions about P fertiliser applications for wheat at sowing.