Context Irrigated dry-season (Boro) rice (Oryza sativa L.) is a major contributor to food security in Bangladesh and inland West Bengal, but its cultivation is limited in the coastal Sundarbans due to high salinity, tidal influences, and scarcity of fresh irrigation water. Current policy frameworks promoting uniform transplanting windows and cultivar choices disregard the marked spatial heterogeneity of abiotic stress regimes in these deltaic agroecosystems. Aims This study aimed to characterize genotype × environment × management (G × E × M) interactions and identify location-specific sowing date–cultivar combinations to improve Boro rice productivity, irrigation water productivity, and yield stability under coastal saline conditions. Methods The Agricultural Production Systems Simulator (APSIM-Oryza) was calibrated and validated using multi-location field experimental data from Bangladesh and India collected during 2016–2018. Model performance was evaluated for phenology, above-ground biomass, and grain yield, with root mean square errors (RMSEs) of 6.7 days, 996 kg ha−1, and 1574 kg ha−1, respectively. Subsequently, 35 years of climate-driven simulations (1984–2018) were conducted to assess the effects of sowing dates and cultivar choices on crop performance, stress exposure, irrigation requirements, and irrigation water productivity across contrasting environments. Results Thirty-five years of climate-driven simulations (1984–2018) showed strong spatiotemporal variability in stress exposure. Early sowing (October) predisposed crops to cold-induced spikelet sterility, whereas late sowing (mid-December onwards) intensified exposure to terminal salinity and heat stress, elevating irrigation demand while reducing water productivity. Mid-November sowing consistently minimized risk trade-offs, achieving the highest probabilities of exceeding 5.0 Mg ha−1 with superior irrigation water productivity (9–11 kg grain ha−1 mm−1). Salt-tolerant cultivars (e.g. BRRI Dhan67, WGL20471) maintained yield across variable stress environments, whereas susceptible genotypes (e.g. BRRI Dhan28, IET4786) exhibited high interannual yield variability and frequent failure. Conclusions Sowing date and cultivar selection jointly determine Boro rice productivity and yield stability in coastal saline environments. Mid-November sowing, combined with salt-tolerant cultivars, emerged as a promising strategy for reducing climatic and salinity-related production risks. However, the magnitude of these benefits depends on local stress conditions and hydrological characteristics. Implications The findings support a shift from blanket regional recommendations towards dynamic, simulation-based decision-support systems that integrate sowing windows, stress-resilient cultivars, and location-specific hydrological and salinity conditions. Integrating these systems into agricultural extension services and policy frameworks could improve climate resilience, irrigation water productivity, and sustainable Boro rice production across the coastal saline zones of South Asia.
Context Australia’s dairy production relies predominantly on grazed rainfed perennial ryegrass pastures. Multi-species pastures, including grasses, legumes, and herbs, have potential to improve pasture dry-matter (DM) production and nutritional value, relative to perennial ryegrass pastures. However, there are limited data on their performance under Australian conditions. Aims To assess pasture yield and nutritive characteristics of multi-species pastures compared with perennial ryegrass pastures on Victorian dairy farms. Methods A paired paddock approach over 18–24 months (June 2022 to June 2024) across 10 commercial dairy farms was used. Seasonal DM production and nutritive characteristics were assessed, and pasture type × season interactions were tested. Key results Significant pasture type effects were observed at six of the nine farms, either as main effects or through interactions with season and/or year. At Barongarook West, multi-species pastures outperformed perennial ryegrass pastures notably in summer 2023/24 (3.52 vs 1.76 Mg DM ha−1). At Tallandoon and Towong Upper, multi-species pastures produced biomass in autumn 2024 (2.86 and 0.23 Mg DM ha−1 respectively), whereas perennial ryegrass did not grow, demonstrating better production under challenging conditions. Where conditions were favourable (e.g. spring 2023), no significant difference between pasture types was observed at six of the nine farms. Crude protein tended to be higher in multi-species pastures, particularly in summer, whereas metabolisable energy generally did not differ between pasture types. Conclusions Multi-species pastures did not consistently increase total DM production relative to perennial ryegrass pastures over the study period, but increased seasonal DM production at several farms, particularly during summer and autumn. Maintenance of species diversity, especially legumes and herbs, is required for their ongoing performance. Implications Multi-species pastures provide one solution to improving year-round feed in dairy pastures under increasingly variable Australian climates.
Context Intercropping can improve resource-use efficiency, yield stability, and profitability, but pulse–pulse intercrops remain poorly studied in mechanised dryland systems. Aims To determine whether functional complementarity among pulse–pulse and pulse–oilseed crops improves yield, nutrient uptake, phenological risk buffering, and profitability under rainfed conditions. Methods Field experiments conducted in southern Australia from 2019 to 2024 compared monocultures with pulse–pulse (lentil–faba bean; faba bean–field pea) and oilseed–pulse (canola–field pea, canola–faba bean, linseed–chickpea) intercrops across locations and sowing dates. Key results Functionally divergent intercrops such as canola–field pea recorded land equivalent ratios (LER) of >1 and nutrient uptake LERs for nitrogen, phosphorus, and sulfur that exceeded unity. By contrast, chickpea–linseed and lentil–faba bean intercrops underperformed with LERs of <1. Faba bean–field pea mixtures yielded similarly to or better than sole crops across environments, with mean LERs near or above 1. Field pea performed better in low-rainfall environments, whereas faba bean performed better in favourable conditions. These complementary responses reflected phenological divergence, with faba bean flowering earlier and field pea maturing sooner. Grain protein concentration remained stable across faba bean–field pea mixtures and was environment-driven. Gross margins were competitive and stable, with some mixtures outperforming monocultures in both yield and profitability. Conclusions Pulse–pulse intercropping offers a low-barrier diversification strategy for mechanised dryland systems. Functional complementarity underpinned agronomic and economic benefits, with nutrient uptake advantages greatest in functionally distinct oilseed–pulse intercrops. Implications Intercropping can improve productivity, profitability, and resilience in low-input dryland systems, although benefits depend strongly on species selection and functional contrast.
Context Maize (Zea mays) is a major source of green fodder and silage for livestock systems, but simultaneous improvement of biomass yield, stability, and feed quality remains challenging due to the complex nature of these traits and their sensitivity to environmental variation. Aims This study aimed to assess genetic variability, trait associations, and silage quality in maize hybrids and to identify superior and stable hybrids using the Factor Analysis and Ideotype Design based on Best Linear Unbiased Prediction (FAI-BLUP) multi-trait ranking index. Methods Twelve maize hybrids were evaluated during two spring seasons for 10 agro-morphological traits and key silage-quality parameters. Statistical analyses included descriptive statistics, ANOVA, correlation analysis, principal component analysis, and a factor analysis and ideotype-design based multi-trait selection index (FAI-BLUP) to rank hybrids for combined performance and consistency across environments. Key results Significant genetic variability was observed for fodder yield, plant architecture, and silage-quality traits. Green fodder yield showed strong positive associations with plant height, leaf length, leaf width, and number of leaves. Principal component analysis revealed that phenology and canopy-related traits accounted for most of the variation among hybrids. The FAI-BLUP multi-trait ranking index identified hybrids JH 17026 and PMH 10 as superior for overall multi-trait performance, while hybrids LH 114 and JH 17011 exhibited high consistency across seasons. Conclusions Integrated multivariate analysis and FAI-BLUP effectively identified and ranked maize hybrids combining high fodder yield, consistency, and improved silage quality. Implications The results provide a robust framework for fodder maize breeding and support the further evaluation of high-performing, stable hybrids to enhance productivity and nutritional quality in livestock production systems.
Context The Democratic People’s Republic of Korea (DPRK) is vulnerable to drought due to monsoon-driven variable precipitation that affects agricultural production and water security. Reliable multi-timescale drought forecasting aids risk mitigation; however, nonlinear non-stationary drought signals are hard to capture indata-scarce regions. Aims This study developed and validated a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Long Short-Term Memory (CEEMDAN-LSTM) hybrid Standardised Precipitation Evapotranspiration Index (SPEI) drought forecasting model. Its performance is systematically compared against a standalone LSTM for multiple SPEI timescales under DPRK climatic conditions. Methods Monthly meteorological records from 10 stations collected over a period of 60 years were used to derive SPEI time series. CEEMDAN decomposed SPEI into intrinsic mode functions for noise reduction and trend extraction as LSTM inputs; hyperparameters were optimised by Bayesian optimisation. Model performance was assessed with Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Key results CEEMDAN-LSTM generally outperformed standalone LSTM with timescale-dependent skill. Short-term SPEI predictions had inherent accuracy limits (NSE < 0.5) yet reduced relative errors. Medium- to long-term SPEI yielded large accuracy gains (max NSE = 0.95 for SPEI-12), with residual-distribution analysis showing effective suppression of extreme prediction errors. Spatial heterogeneity existed; complex terrain at stations such as Chongjin limited decomposition-driven improvements. Conclusions CEEMDAN-LSTM improves forecast skill by processing non-stationary drought signals. Without complex external predictors, this hybrid method enables reliable medium-long-term drought monitoring and extreme-event detection. Implications This single-variable time-series method enables data-efficient drought early warning for data-scarce regions, supporting drought response and adaptive water management without large-scale remote-sensing or global datasets.
