SUMMARY: A warmer and potentially drier future climate is likely to influence the production of forage crops on dairy farms in the southeast dairy regions of Australia. Biophysical modelling was undertaken to explore the resilience of forage production of individual forage crops to scalar increases in temperature, atmospheric carbon dioxide (CO₂) concentration and changes in daily rainfall. The model APSIM was adapted to reflect species specific responses to growth under elevated atmospheric CO₂ concentrations. It was then used to simulate 40 years of production of forage wheat, oats, annual ryegrass, maize grown for silage, forage sorghum, forage rape and alfalfa grown at three locations in southeast Australia with increased temperature scenarios (1, 2, 3 and 4 °C of warming) and atmospheric CO₂ concentration (435, 535, 640 and 750 ppm) and decreasing rainfall scenarios (10, 20 or 30% less rainfall). At all locations positive increases in DM yield compared with the baseline climate scenario were predicted for lucerne (2·6–93·2% increase), wheat (8·9–37·4% increase), oats (6·1–35·9% increase) and annual ryegrass (9·7–66·7% increase) under all future climate scenarios. The response of forage rape and forage sorghum varied between location and climate change scenario. At all locations, maize was predicted to have a minimal change in yield under all future climates (between a 2·6% increase and a 6·8% decrease). The future climate scenarios altered the seasonal pattern of forage supply for wheat, oats and lucerne with an increase in forage produced during winter. The resilience of forage crops to climate change indicates that they will continue to be an important component of dairy forage production in southeastern Australia.

There is interest in the reincorporation of legumes and forbs into pasture-based dairy production systems as a means of increasing milk production through addressing the nutritive value limitations of grass pastures. The experiments reported in this paper were undertaken to evaluate milk production, blood metabolite concentrations, and forage intake levels of cows grazing either pasture mixtures or spatially adjacent monocultures containing perennial ryegrass (Lolium perenne), white clover (Trifolium repens), and plantain (Plantago lanceolata) compared with cows grazing monocultures of perennial ryegrass. Four replicate herds, each containing 4 spring-calving, cross-bred dairy cows, grazed 4 different forage treatments over the periods of early, mid, and late lactation. Forage treatments were perennial ryegrass monoculture (PRG), a mixture of white clover and plantain (CPM), a mixture of perennial ryegrass, white clover, and plantain (RCPM), and spatially adjacent monocultures (SAM) of perennial ryegrass, white clover, and plantain. Milk volume, milk composition, blood fatty acids, blood β-hydroxybutyrate, blood urea N concentrations, live weight change, and estimated forage intake were monitored over a 5-d response period occurring after acclimation to each of the forage treatments. The acclimation period for the early, mid, and late lactation experiments were 13, 13, and 10 d, respectively. Milk yield (volume and milk protein) increased for cows grazing the RCPM and SAM in the early lactation experiment compared with cows grazing the PRG, whereas in the mid lactation experiment, milk fat increased for the cows grazing the RCPM and SAM when compared with the PRG treatments. Improvements in milk production from grazing the RCPM and SAM treatments are attributed to improved nutritive value (particularly lower neutral detergent fiber concentrations) and a potential increase in forage intake. Pasture mixtures or SAM containing plantain and white clover could be a strategy for alleviating the nutritive limitations of perennial ryegrass monocultures, leading to an increase in milk production for spring calving dairy cows during early and mid lactation.

The demand for dairy products is ever increasing across the world. The livestock sector is a significant source of greenhouse gas (GHG) emissions globally. The availability of high-quality pasture is a key requirement to increase the productivity of dairy cows as well as manage enteric methane emissions. Warm-season perennial grasses are the dominant forages in tropical and subtropical regions, and thus exploring their nutritive characteristics is imperative in the effort to improve dairy productivity. Therefore, we have collated a database containing a total of 4750 records, with 1277 measurements of nutritive values representing 56 tropical pasture species and hybrid cultivars grown in 26 different locations in 16 countries; this was done in order to compare the nutritive values and GHG production across different forage species, climatic zones, and defoliation management regimes. Average edaphoclimatic (with minimum and maximum values) conditions for tropical pasture species growing environments were characterized as 22.5 °C temperature (range 17.5–29.30 °C), 1253.9 mm rainfall (range 104.5–3390.0 mm), 582.6 m elevation (range 15–2393 m), and a soil pH of 5.6 (range 4.6–7.0). The data revealed spatial variability in nutritive metrics across bioclimatic zones and between and within species. The ranges of these nutrients were as follows: neutral detergent fibre (NDF) 50.9–79.8%, acid detergent fibre (ADF) 24.7–57.4%, crude protein (CP) 2.1–21.1%, dry matter (DM) digestibility 30.2–70.1%, metabolisable energy (ME)3.4–9.7 MJ kg⁻¹ DM, with methane (CH₄) production at 132.9–133.3 g animal⁻¹ day⁻¹. The arid/dry zone recorded the highest DM yield, with decreased CP and high fibre components and minerals. Furthermore, the data revealed that climate, defoliation frequency and intensity, in addition to their interactions, have a significant effect on tropical pasture nutritive values and CH₄ production. Overall, hybrid and newer tropical cultivars performed well across different climates, with small variations in herbage quality. The current study revealed important factors that affect pasture nutritive values and CH₄ emissions, with the potential for improving tropical forage through the selection and management of pasture species.

Irrigated cotton (Gossypium hirsutum L.) growers in the Murray-Darling Basin (MDB) of Australia, are challenged by limited water availability. This modelling-study aimed to determine if deficit irrigation (DI) practices can potentially improve water use efficiency (WUE) for furrow irrigation (FI), overhead sprinkler irrigation (OSI) and subsurface drip irrigation (SDI) systems. We validated the Agricultural Production System sIMulator (APSIM) against observed cotton lint yield and crop biomass accumulation for different management practices. The model achieved concordance correlation coefficients of 0.93 and 0.82 against observed cotton crop biomass accumulation and lint yields, respectively. The model was then applied to evaluate the impacts of different levels of DI on lint yield, WUE across cotton growing locations in the MDB (Goondiwindi, Moree, Narrabri, and Warren), during the period from 1977 to 2017. The different levels of DI for the FI system were no irrigation, full irrigation (TF) and irrigated one out of four, one out of three, one out of two, two out of three and two out of four TF events. For the OSI and SDI systems, DI levels were no irrigation, TF, 20% of TF, 40% of TF, 60% of TF and 80% of TF. Lint yield was maximised under the OSI and SDI systems for most locations by applying 80% of TF. However; modelling identified that WUE was maximised at 60% of full irrigation for OSI and SDI systems. These results suggest there are significant gains in agronomic performance to be gained through the application of DI practices with these systems. For FI, DI had no benefit in terms of increasing yield, while DI showed marginal gains in terms of WUE in some situations. This result is due to the greater exposure to periodic water deficit stress that occurred when DI practices were applied by an FI system. The results suggest that in the northern MDB, water savings could be realised for cotton production under both OSI and SDI systems if DI were adopted to a limited extent, depending on location and irrigation system.

Integrating weather forecasts into decision support systems empowers farmers to optimise irrigation schedules, thereby boosting crop yields and conserving water. However, inaccurate forecasts can jeopardise productivity and irrigation efficiency. This study combines a crop model with a stochastic pseudo-weather forecast algorithm to: (1) determine the reliability needed in a weather forecast algorithm for effective irrigation management; and (2) assess the impact of weather forecast reliability on the productivity and environmental footprint of various maize cropping systems across diverse climates. It employs the Next Generation of Agricultural Production Systems sIMulator (APSIM NextGen) to simulate maize growth at eleven locations representing diverse climates globally. Various planting schedules, soil types, irrigation systems, and nitrogen availability levels were considered to examine the effects of perfect and imperfect weather forecasts. The findings underscore the potential of integrating weather forecasts into irrigation management for enhanced productivity and sustainability. High-confidence forecasts and longer lead times increase yields (up to 11 %) and improve sustainability outcomes, particularly in wetter climates and for conditions with low nitrogen availability. Conversely, when the accuracy of forecasts is low, forecast-driven irrigation management may lead to yield reductions compared to a baseline system, especially in drier climates (up to 26 % reduction), necessitating tailored management strategies. Soil type and farmer's risk tolerance further influence the effectiveness of forecast-driven irrigation management, emphasising the need for context-specific approaches. By understanding and leveraging the interconnected impacts of weather forecasts on yield, water use efficiency, nitrogen loss, and greenhouse gas emissions, farmers can optimise productivity while minimising environmental impacts.