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Simple Summary: Adopting deficit irrigation (DI) to improve crop production and safeguard groundwater resources is of great importance in water scarce regions, e.g., the North China Plain (NCP). Under the background of global warming, it is worth investigating whether DI continues to play such a key role under future climate change scenarios. Thus, we studied the effect of DI on wheat yield and water use efficiency under future climate change scenarios. We found that moderate deficit irrigation (DI3, ≤0.4 PAWC at sowing to flowering stage) under the N3 (150 kg N ha−1) condition was identified as the optimum irrigation schedule for the study site under future climate change scenarios. However, the compensation effect of DI3 on yield and water use efficiency (WUE) became weak in the future. To conclude, in water scarce regions of NCP, DI remains an effective strategy to maintain higher yield and enhance water use under future climate scenarios. Background. Deficit irrigation (DI) is a feasible strategy to enhance crop WUE and also has significant compensation effects on yield. Previous studies have found that DI has great potential to maintain crop production as full irrigation (FI) does. Therefore, adopting DI to improve crop production and safeguard groundwater resources is of great importance in water scarce regions, e.g., the North China Plain (NCP). Under the background of global warming, it is worth investigating whether DI continues to play such a key role under future climate scenarios. Methods. We studied the response of winter wheat yield and WUE to different DI levels at pre-anthesis under two Shared Socioeconomic Pathways (SSPs) scenarios (SSP245 and SSP585) using the Agricultural Production Systems Simulator (APSIM) model driven by 21 general circulation models (GCMs) from the Coupled Model Inter-Comparison Project phase 6 (CMIP6). Additionally, we explored the effects of different nitrogen (N) fertilizer application rates on DI. Results. We found that simulated wheat yield would increase by 3.5–45.0%, with WUE increasing by 8.8–46.4% across all treatments under future climate change. Moderate deficit irrigation (DI3, ≤0.4 PAWC at the sowing to flowering stage) under the N3 (150 kg N ha−1) condition was identified as the optimum irrigation schedule for the study site under future climate change. However, compensation effects of DI3 on yield and WUE became weak in the future, which was mainly due to increased growing season rainfall projected by GCMs. In addition, we found that N fertilizer application could mitigate the effect of DI3. Conclusions. We highlight that in water scarce regions of NCP, DI remains an effective strategy to maintain higher yield and enhance water use under future climate scenarios. Results strongly suggest that moderate deficit irrigation under a 150 kg N ha−1 condition could mitigate the contradiction between production and water consumption and ensure food safety in the NCP. [ABSTRACT FROM AUTHOR]

Spatial modelling of agricultural production has been more frequently analysed to assist with regional and long‐term planning. Typically, point‐scale crop models are used to construct these analyses with researchers using various sources of input data, including observed and interpolated gridded climate data. Understanding the implication of data choice on production estimates is crucial to appreciate the consequences of selecting methodological approaches for agricultural model outputs. In this study, we compared site observed climate data and gridded data sets interpolated from site observations that are commonly used the Australian climate data set. We assessed the consequences of the differences in climate variables (biases) to the modelled outputs. Our results showed that the major differences between gridded and observed climate data sets were for rainfall variables. Interpolated gridded data tended to have larger rainfall frequency and smaller rainfall intensity, leading to lower surface runoff and higher soil evaporation that caused less plant water uses and less nitrogen uptake, and ultimately resulted in biases in simulated crop biomass and yield. In addition, the results indicated that agricultural models implemented with gridded data could produce similar overall model outputs as those driven by observed climate data, but can result in more uncertainties in simulated spatial outputs. This is particularly evident in regions with high rainfall. Our results showed that applying agricultural models with observed data and then interpolating these results spatially might be the optimal approach to minimize the biases in production modelling outputs and reduce computing time and storage. [ABSTRACT FROM AUTHOR]

• A hybrid model was developed by integrating the APSIM model and the RF model • The hybrid model outperformed the APSIM model in predicting observed wheat yield • The APSIM model might underestimate future yield losses caused by climate extremes • Increasing heat events were identified to be the major factor causing future yield losses Accurately assessing the impacts of extreme climate events (ECEs) on crop yield can help develop effective agronomic practices to deal with climate change impacts. Process-based crop models are useful tools to evaluate climate change impacts on crop productivity but are usually limited in modelling the effects of ECEs due to over-simplification or vague description of certain process and uncertainties in parameterization. In this study, we firstly developed a hybrid model by incorporating the APSIM model outputs and growth stage-specific ECEs indicators (i.e. frost, drought and heat stress) into the Random Forest (RF) model, with the multiple linear regression (MLR) model as a benchmark. The results showed that the APSIM + RF hybrid model could explain 81% of the observed yield variations in the New South Wales wheat belt of south-eastern Australia, which had a 33% improvement in modelling accuracy compared to the APSIM model alone and 19% improvement compared to the APSIM + MLR hybrid model. Drought events during the grain-filling and vegetative stages and heat events immediately prior to anthesis were identified as the three most serious ECEs causing yield losses. We then compared the APSIM + RF hybrid model with the APSIM model to estimate the effects of future climate change on wheat yield. It was interesting to find that future yield projected from single APSIM model might have a 1–10% overestimation compared to the APSIM + RF hybrid model. The APSIM + RF hybrid model indicated that we were underestimating the effects of climate change and future yield might be lower than predicted using single APSIM informed modelling due to lack of adequately accounting for ECEs-induced yield losses. Increasing heat events around anthesis and grain-filling periods were identified to be major factors causing yield losses in the future. Therefore, we conclude that including the effects of ECEs on crop yield is necessary to accurately assess climate change impacts. We expect our proposed hybrid-modelling approach can be applied to other regions and crops and offer new insights of the effects of ECEs on crop yield. [ABSTRACT FROM AUTHOR]

Abstract Global food demand is increasing with the rapid growth of the world's population and improvement in living standards. To meet this demand, crop yields need to increase but climate change presents a potential threat. Genetic and agronomic strategies are helping agriculture adapt to climate change, but introducing new genetic traits into crops is time-consuming and costly. Process-based biophysical modelling is a powerful tool for targeting and accelerating development of new synthetic cultivars, and we have used it to identify the traits of rain-fed wheat ideotypes and suitable sowing dates needed to adapt to future climate change in south-eastern Australia. Our simulations involved two Global Climate Models (GCMs) with the driest conditions under a high emission scenario of Representative Concentration Pathway (RCP) 8.5. We compared simulated yields under future climate with those under historical climate with and without changes in cultivar and sowing date. Our results show that wheat yield for the reference cultivar would decrease on average by 23% and 38% in 2061–2100 under RCP8.5 at two contrasting sites (wet and dry, respectively). Ideotypes with an early flowering date, longer grain filling period, larger radiation use efficiency, larger maximum grain size and faster potential grain filling rate, sown on the optimum sowing date proved to be effective at the wet site in reversing these declines, leading to an average yield increase of 20–24% for both GCMs. However, improving cultivars and altering sowing times would have little impact for a drier GCM at the dry site. Although there is some uncertainty in simulations related to the genetic coefficients used in the crop model, climate projections and emission scenarios, we demonstrate that it is possible to enhance wheat production under a future climate if a cultivar with a longer grain filling period and larger yield component parameter was adopted in eastern Australian wheat-growing areas. Highlights • Two extreme dry GCMs under RCP8.5 scenario are selected to analyse. • The current cultivar may not be suitable for the future dry climate conditions. • The ideotypes proposed have an early anthesis and a longer grain filling period. • The ideotypes have a larger RUE, max grain size and potential grain filling rate. • The adaptation options proposed hardly combat climate change at a drier site. [ABSTRACT FROM AUTHOR]

• A climate-crop modelling approach was proposed to assess wheat vulnerability. • Historically, the most vulnerable areas were the western parts of the wheat belt. • High vulnerability would expand to eastern parts of the wheat belt into the future. • Adaptation options had limited potential to decrease future vulnerability. Assessing the vulnerability of agriculture to climate change can identify knowledge gaps and direct where adaptation options are required. However, determining the vulnerability of agricultural production to climate change is a challenge due to the complex nature of the problem. We propose a climate-crop modelling approach illustrated by using the APSIM crop model forced with daily climate data as a case study in eastern Australia to assess the biophysical vulnerability of wheat to future climate change. This impact assessment is based on the Intergovernmental Panel on Climate Change's definition of vulnerability as a derivative of exposure, sensitivity and adaptive capacity. We selected one Global Climate Model (GCM) CSIRO-Mk3-6-0 representing dry conditions under high emission scenarios to explore the feasibility of proposed methodology. Our results show that, historically, the most vulnerable areas were the western parts of the wheat belt, accounting for 18% of the total wheat-growing area. However, areas of very high vulnerability would expand to central and eastern parts of the wheat belt into the future, accounting for 41% of the study area in the 2040s and 62% in the 2080s. With increased exposure to dry climate, adjusting sowing time and cultivar shift consistently improved wheat yield but were insufficient to decrease wheat vulnerability. We found the key driver for increased vulnerability was increased exposure to dry climate. We expect this study will help growers, farm advisors and state policymakers to adopt relevant adaptation strategies and promote efficient farming practices such as breeding drought-tolerant crop cultivars in highly vulnerable western areas. [ABSTRACT FROM AUTHOR]

Early and reliable seasonal crop yield forecasts are crucial for both farmers and decision-makers. Commonly-used methods for seasonal yield forecasting are based on process-based crop models or statistical regression-based models. Both have limitations, particularly in regard to accounting for growth stage-specific climate extremes (such as drought, heat, and frost). In this study, we firstly developed a hybrid yield forecasting approach by blending of multiple growth stage-specific indicators, i.e. APSIM (a process-based crop model)-simulated biomass, and climate extremes, NDVI (Normalized Difference Vegetation Index), and SPEI (Standardized Precipitation and Evapotranspiration Index) before forecasting dates, using a regression model (random forest or multiple linear regression). Plot-scale wheat yield (2008–2017) in the southeastern Australian wheat belt was dynamically forecasted at the end of several targeted growth stages as the growing season progressed to harvest. Results showed that the forecasting accuracy increased significantly for both systems as forecast time approached harvest time. The forecasting system based on random forest outperformed the forecasting system based on multiple linear regression at each forecasting event. Satisfactory yield forecasts occurred at one month (~35 days) prior to harvest (r = 0.85, LCCC = 0.81, MAPE = 17.6%, RMSE = 0.70 t ha−1, and ROC score = 0.90), and at two months before harvest (r = 0.62, LCCC = 0.53, MAPE = 27.1%, RMSE = 1.01 t ha−1, and ROC score = 0.88). In addition, drought events throughout the growing season were identified as the main factor causing yield losses in the wheat belt during the past decade. With the increasing availability of farming-related data, we expect that the yield forecasting system proposed in our study may be widely extended to other comparable cropping regions to produce sufficiently accurate wheat yield forecasts for stakeholders to develop strategic decisions in their respective roles. [ABSTRACT FROM AUTHOR]

The impacts of compound drought-heat (DH) events on crops are more devastating than a single extreme event of drought or heat. Previous studies mainly assessed the change of individual extreme climate events. However, studies quantifying the characteristics of DH events during crop growth periods are still lacking. To the best of our knowledge, there is no study that has quantified the potential of adjusting sowing time and changing cultivars to reduce the risk of DH events for wheat in Australia. We aimed to (1) develop a combined index to capture the DH events occurring during the wheat flowering period at six study sites in southeastern Australia's wheat belt; (2) quantify the changes in the frequency (DHF), duration (DHD), and intensity (DHI) of DH events under future climate; and (3) identify feasible agronomic options to reduce the risk of DH events. We used the APSIM model driven by climate projections from 27 GCMs for the period of 1981–2100 to simulate the wheat flowering time and daily plant available water (PAW) at 0–100 cm soil layer. The wheat sensitive period (WSP) was defined as the period from 2 weeks before flowering to 2 weeks after flowering time. A DH event occurs when the daily maximum temperature is higher than 28 ℃ and daily PAW is less than 40% of plant available water capacity for three consecutive days or more. Then, we assessed the DHF , DHD , and DHI under projected climate change. Finally, we investigated the potential of changing sowing time and cultivars to alleviate DHF , DHD , and DHI under different climate scenarios. According to the average values across six sites, the DHF , DHD , and DHI during the WSP increased by 15%, 12%, and 0.9% in 2040 s, and 49%, 44%, and 5% in 2080 s, respectively, compared to 2000 s. Such increases in DH events were mainly due to enhanced heat events. Early sowing and planting better-performing wheat cultivars with early flowering had great potential to lower the risk of future DH events. They could reduce the DHF , DHD , and DHI by 15%− 100%, 18%− 100%, and 16%− 100%, respectively, compared to without adaptation options. However, the strategy may introduce an increased frost risk across six sites, especially in regions with climates that are less dry and hot, such as Mudgee and Wagga Wagga. We expect our modelling work can provide useful information for developing effective agronomic management practices to help Australian wheat growers better prepare for DH events under climate change. The newly proposed DH framework can be applied to other dryland wheat growing regions globally. • Compound drought-heat (DH) events during wheat flowering period are analyzed. • The DH events are likely to be more frequent, prolonged, and intensified under climate change. • The increased DH events are mainly due to enhanced heat events in the future. • Changing sowing time and cultivar can effectively lower the risk of future DH events. [ABSTRACT FROM AUTHOR]

Conservation agriculture management practices (e.g., cover crops and residue retention) have been widely promoted to improve soil quality and environmental sustainability. However, little is known about the long-term interactive effects of cover crops and residue retention on yield of the cash crops and environmental outcomes in dryland cropping systems under climate change. We used the pre-validated APSIM model, driven by statistically downscaled daily climate data from 27 Global Climate Models (GCMs) under two Shared Socioeconomic Pathways (SSP245 and SSP585), to assess the combined influences of cowpea cover crops and three residue retention levels on soil water balance, soil organic carbon (SOC), nitrogen (N) dynamics, crop yield and gross margin across six crop rotation systems during the historical period (1985–2020), near future (2021–2056), and far future (2057–2092) in southeast Australia. Our results showed that, on average, cover crops decreased soil moisture on the day of sowing the succeeding cash crop (by 22%), but led to greater SOC stock (21%), reduced N loss through leaching (71%), and enhanced N uptake and yield of cereals, but decreased N uptake and yield of field pea. The effects of cover crops on yield and gross margin became more positive in the far future under both SSPs, which may be attributed to the SOC increase and greater N availability in the long term. These benefits were more evident under residue removal due to the partly compensatory effects from cover crop residues. Furthermore, cover crops were profitable in the wetter parts of the study region (east), but reduced gross margin in the drier west due to depletion of soil water reserves for the next cash crop. We conclude that particularly where residues are removed, the long-term adoption of cowpea cover crops could be a potential practice to sustain crop productivity with environmental co-benefits under climate change in the wetter parts of the dryland cropping region of southeast Australia. • Cover crops increase SOC, N availability, and cash crop yields except for field pea. • Benefits to yield increase under climate change but decrease with residue retention. • Cover crops are profitable in the wetter area but not in the drier part of the study region. • Long-term use of cover crops may achieve co-benefits for production and environment. [ABSTRACT FROM AUTHOR]