Evaluating nitrogen dynamic and utilization under controlled-release fertilizer application for sunflowers in an arid region: Experimental and modeling approach.

Traditional nitrogen fertilizers (TNF), such as urea, percolate easily in arid fields, posing low nitrogen use efficiency (NUE) and a high non-point pollution risk. Controlled-release fertilizers (CRF) exhibit significantly lower deep seepage, rendering it a favorable choice in arid fields due to its ability to enhance NUE through slow-release mechanisms. However, current models do not fully account for the soil nitrogen dynamics and crop interactions under controlled-release conditions, and lack quantification. This study improved the APSIM model by adjustment the urea hydrolysis rate to assess the impact of CRF and TNF applications on soil health, crop growth, and water quality. Calibration and validation were conducted through experiments in the Hetao Irrigation District of China from 2019 to 2020, with different nitrogen application rates (135, 225, and 315 kg/ha). The model accurately simulated soil NO3-N concentration (SNC), cumulative NO3-N leaching (CNL), nitrogen uptake (NU), and sunflower yield. During the validation process, R2 and Nash-Sutcliffe efficiency (NSE) values were both above 0.75. Results indicated that the average SNC, NU, and yield under CRF application were significantly higher than those under TNF application, with increases of 38.62%, 44.92%, and 18.38%, respectively. Notably, the proportion of soil nitrogen available (PSNA), a novel metric proposed in this study, was 159.50% higher in the 0-40 cm soil layer with CRF compared to TNF. Additionally, CNL and NO3-N leaching loss rate (NLLR) decreased by 25.76% and 25.77%, respectively. Scenario simulations indicated that the optimal fertilization strategy for this region is to use 180-193.5 kg/ha of CRF with a release period of 80-85.5 d to balance agricultural productivity and ecological protection. This study confirms the significant advantages of CRF in enhancing yield, improving nitrogen management, and promoting environmental sustainability, providing a scientific basis for CRF management strategies and supporting the shift towards more efficient and environmentally friendly agricultural practices.

Simulating the effects of optimizing sowing date and variety shift on maize production at finer scale in northeast China under future climate.

BACKGROUND: Global warming and the rising occurrences of climate extremes have become formidable challenges for maize production in northeast China. The optimization of sowing date and variety choice stand out as two economic approaches for maize to enhance its resilience to climate change. Nevertheless, assessment of the potential of optimizing sowing date and variety shift on maize yield at finer scale remains underexamined. This study investigated the implications of optimizing sowing date and implementing variety shift on maize yield from a regional perspective. RESULTS: Compared to the reference period (1986-2005), climate change would decrease by 11.5-34.6% (the range describes the differences among climate scenarios and agro-ecological regions) maize yield in the 2050s (2040-2059) if no adaption measure were to be implemented. The combined adaption (optimizing sowing date and variety shift) can improve maize yield by 38.8 ± 11.3%, 42.7 ± 9.7% and 33.9 ± 7.6% under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively. The current sowing window typically falls within the projected optimal sowing window, defined as the period capable of achieving 90% of the maximum yield within the potential sowing window under future climate conditions. Consequently, the potential of the effect of optimizing sowing window on maize yield is limited. In contrast, variety shift results in higher yield improvement, as temperature rise creates favorable conditions for transplanting varieties with an extended growth period, particularly in high latitudes and mountainous regions. Under future climate, cumulative precipitation and compound drought and hot days during maize growing seasons are two key factors influencing maize production. CONCLUSIONS: The optimization of sowing date and variety choice can improve maize yield in northeast China. In addition, maize production should consider varieties with longer growth period and drought and heat tolerance to adapt to climate change. © 2023 Society of Chemical Industry.

A cross-scale analysis to understand and quantify the effects of photosynthetic enhancement on crop growth and yield across environments.

Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying the importance of individual and multiple manipulations on the seasonal biomass growth and yield performance of target crops across variable production environments is limited. Using a state-of-the-art cross-scale model in the APSIM platform we predicted the impact of altering photosynthesis on the enzyme-limited (Ac ) and electron transport-limited (Aj ) rates, seasonal dynamics in canopy photosynthesis, biomass growth, and yield formation via large multiyear-by-location crop growth simulations. A broad list of promising strategies to improve photosynthesis for C3 wheat and C4 sorghum were simulated. In the top decile of seasonal outcomes, yield gains were predicted to be modest, ranging between 0% and 8%, depending on the manipulation and crop type. We report how photosynthetic enhancement can affect the timing and severity of water and nitrogen stress on the growing crop, resulting in nonintuitive seasonal crop dynamics and yield outcomes. We predicted that strategies enhancing Ac alone generate more consistent but smaller yield gains across all water and nitrogen environments, Aj enhancement alone generates larger gains but is undesirable in more marginal environments. Large increases in both Ac and Aj generate the highest gains across all environments. Yield outcomes of the tested manipulation strategies were predicted and compared for realistic Australian wheat and sorghum production. This study uniquely unpacks complex cross-scale interactions between photosynthesis and seasonal crop dynamics and improves understanding and quantification of the potential impact of photosynthesis traits (or lack of it) for crop improvement research.

Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to introduce biological constraints in a synthetic training dataset. In addition to the comparison of two datasets without and with biological constraints, we also investigated the effects of observation geometry, retrieval method, and wavelength range on estimation accuracy of four crop traits (leaf area index, leaf chlorophyll content, leaf dry matter, and leaf water content) of wheat. The theoretical analysis demonstrated potential advantages of adding biological constraints in synthetic training datasets as well as the capability of deep learning. Additionally, the predictive models were validated on real unmanned aerial vehicle-based multispectral images collected from wheat plots contrasting in canopy structure. The predictive model trained over a synthetic dataset with biological constraints enabled the prediction of leaf water content from using wavelengths in the visible to near infrared range based on the correlations between crop traits. Our findings presented the potential of the proposed conceptual framework in simultaneously retrieving multiple crop traits from canopy reflectance for applications in precision agriculture and plant breeding.

Modelling the effects of cold temperature during the reproductive stage on the yield of chickpea (Cicer arietinum L.).

During the reproductive stage, chilling temperatures and frost reduce the yield of chickpea and limit its adaptation. The adverse effects of chilling temperature and frost in terms of the threshold temperatures, impact of cold duration, and genotype-by-environment-by-management interactions are not well quantified. Crop growth models that predict flowering time and yield under diverse climates can identify combinations of cultivars and sowing time to reduce frost risk in target environments. The Agricultural Production Systems Simulator (APSIM-chickpea) model uses daily temperatures to model basic crop growth but does not include penalties for either frost damage or cold temperatures during flowering and podding stages. Regression analysis overcame this limitation of the model for chickpea crops grown at 95 locations in Australia using 70 years of historic data incorporating three cultivars and three sowing times (early, mid, and late). We modified model parameters to include the effect of soil water on thermal time calculations, which significantly improved the prediction of flowering time. Simulated data, and data from field experiments grown in Australia (2013 to 2019), showed robust predictions for flowering time (n = 29; R2 = 0.97), and grain yield (n = 22; R2 = 0.63-0.70). In addition, we identified threshold cold temperatures that significantly affected predicted yield, and combinations of locations, variety, and sowing time where the overlap between peak cold temperatures and peak flowering was minimal. Our results showed that frost and/or cold temperature-induced yield losses are a major limitation in some unexpected Australian locations, e.g., inland, subtropical latitudes in Queensland. Intermediate sowing maximise yield, as it avoids cold temperature, late heat, and drought stresses potentially limiting yield in early and late sowing respectively.

Increased heat stress risk for maize in arid-based climates as affected by climate change: threats and solutions.

Heat stress in combination with drought has become the biggest concern and threat for maize yield production, especially in arid and hot regions. Accordingly, different optimal solutions should be considered in order to maintain maize production and reduce the risk of heat stress under the changing climate. In the current study, the risk of heat stress across Iranian maize agro-ecosystems was analyzed in terms of both intensity and frequency. The study areas comprised 16 provinces and 24 locations classified into five climate categories: arid and hot, arid and temperate, semi-arid and hot, semi-arid and temperate, and semi-arid and cold. The impact of heat stress on maize under a future climate was based on a 5-multi-model ensemble under two optimistic and pessimistic emission scenarios (RCP4.5 and RCP8.5, respectively) for 2040-2070 using the APSIM crop model. Simulation results illustrated that in the period of 2040-2070, intensity and the frequency of heat stress events increased by 2.37 °C and 79.7%, respectively, during maize flowering time compared to the baseline. The risk of heat stress would be almost 100% in hot regions in the future climate under current management practices, mostly because of the increasing high-risk window for heat stress which will result in a yield reduction of 0.83 t ha-1. However, under optimal management practices,farmers will economically obtain acceptable yields (6.6 t ha-1). The results also indicated that the high-risk windows in the future will be lengthening from 12 to 33 days in different climate types. Rising temperatures in cold regions as a result of global warming would provide better climate situations for maize growth, so that under optimistic emission scenarios and optimal management practices, farmers will be able to boost grain yield up to 9.2 t ha-1. Overall, it is concluded that farmers in hot and temperate regions need to be persuaded to choose optimal sowing dates and new maize cultivars which are well adapted to each climate to reduce heat stress risk and to shift maize production to cold regions.

Assessing the performance of two gridded weather data for sugarcane crop simulations with a process-based model in Center-South Brazil.

High-quality measured weather data (MWD) are essential for long-term and in-season crop model applications. When MWD is not available, one alternative for crop simulations is to employ gridded weather data (GWD), which needs to be evaluated a priori. Therefore, this study aimed to evaluate the impact of weather data from two GWD sources (NASA and XAVIER), in the way that they are available for end users, on simulating sugarcane crop performance within the APSIM-Sugar model at traditional sites where sugarcane is grown in Center-South Brazil, compared to simulations with MWD. Besides, this study also evaluated the impact of replacing GWD rainfall by the site-specific measured data on such simulations. A common sugarcane cropping system was repeatedly simulated between 1997 and 2015 for different combinations of climate input. Both NASA and XAVIER appear to be interesting for applications that only require temperature and solar radiation for predictions, such as crop phenology and potential yield. Nonetheless, GWD should be used with caution for crop model applications that rely on accurate estimation of crop water balance, canopy development, and biomass accumulation, at least with crop models that run at a daily time-step. The replacement of gridded rainfall with measured rainfall was pivotal for improving sugarcane simulations, as observed for cane yield, by increasing both agreement (NASA d index from 0.67 to 0.90; XAVIER d from 0.73 to 0.93) and R2 (NASA from 0.35 to 0.76; XAVIER from 0.43 to 0.79) and reducing root mean square errors (RMSE) from 32.8 to 16.3 t/ha when simulated with other variables of NASA data and from 27.9 to 12.7 t/ha when having XAVIER data as input. Therefore, while using both GWD sets without any correction, it is recommended to replace gridded rainfall by measured values, whenever possible, to improve sugarcane simulations in Center-South Brazil.

Quantifying and optimizing agroecosystem services in China's Taihu Lake Basin.

This study examines rice-wheat agroecosystems in the Taihu Lake Basin: one of China's largest commercial grain-farming areas and a region that has faced severe deterioration in water quality. Spatiotemporal changes over the period 1986-2015 in four key ecosystem services (ESs) - grain yield, nitrogen loss, N2O emission, and soil organic carbon (SOC) accumulation - were examined by applying the Agricultural Production Systems Simulator (APSIM) across the basin at county level. Two straw return modes (namely, full straw return versus no return) and three fertilizer-use reduction modes (-5%, -10%, and -20%) were set up to generate six combined scenarios, to propose pathways that reduce the variability of grain production and improve water quality by reducing loss of nitrogen (N loss) - in consideration of the Basin's vital role in agricultural production and the need to protect water quality. Results show that annual grain yield and net five-year difference in SOC accumulation exhibited an overall downward trend from 1986 to 2015, while N2O emission and N loss increased. Two pairs of ESs showed desirable synergies (increasing grain yield and increasing SOC accumulation; decreasing N2O emission and decreasing N loss), encompassing 45.8% and 2.4% of total cultivated land area respectively. Another two pairs exhibited desirable trade-offs (increasing SOC accumulation and decreasing N loss; increasing SOC accumulation and decreasing N2O emission), accounting for 19.0%, and 2.4% of total cultivated land area respectively. There was considerable overlap within counties, which showed high values of grain yield, N2O emission, nitrogen loss, and SOC accumulation in the Basin; but values were relatively high in the east and relatively low in the west. Fertilizer use has significant positive correlations with grain yield and SOC accumulation, and it reduces N loss and N2O emission. Straw return was predicted to raise grain yields and net five-year difference in SOC accumulation and to reduce N loss, but also to increase N2O emissions. Recommended strategies to reduce N loss and stabilize grain supply in the study area are 1) reducing fertilizer use by 20% in areas where N application was above 490 kg N/ha, and 2) implementing straw return and reducing fertilizer use by 5% for areas where N application ranged between 380 and 490 kg N/ha.

Modelling the effect of changing transplanting date on consumptive water footprints for paddy under the system of rice intensification.

BACKGROUND: Consumptive water footprint (CWF) is a comprehensive measure of water consumption by paddy and can be used to assess the impact on freshwater volume. The seasonal water consumption and water footprints of paddy under any irrigation practice vary with changing the transplanting dates. The present study aimed to investigate the impact of shifting transplanting dates on CWFs of paddy under the system of rice intensification (SRI) using a crop model. A medium-duration variety (IR-36) was cultivated during kharif (monsoon) and rabi (non-monsoon) seasons of 2015/16 and 2016/17. The field data were used to calibrate and validate the crop model, Agricultural Production Systems Simulator (APSIM)-Oryza, as well as simulate paddy yield, evapotranspiration and consumptive water footprints (CWFs) under different transplanting dates. RESULTS: The APSIM-Oryza simulated grain yield was found to be closely matched with the observed yield during both calibration (r2  = 0.98, root-mean-square error < 300 kg ha-1 ) and validation (r2  = 0.88, root-mean-square error < 400 kg ha-1 ). The seasonal water savings in SRI practice was 18-21% compared to conventional, with an effect of a 20-30% improvement in the yield. The early transplanting on 1 July in kharif and 15 December in rabi can produce maximum grain yields of 4.55 and 5.15 t ha-1 , respectively, with a minimum CWF of 1064 and 855 m3 t-1 under SRI for the study region. CONCLUSION: The comparison of yield and CWF scenarios under different transplanting dates revealed the superiority of early transplanting in terms of yield improvement with the least irrigation requirement and CWF under SRI. © 2021 Society of Chemical Industry.

A reduced-tillering trait shows small but important yield gains in dryland wheat production.

Reducing the number of tillers per plant using a tiller inhibition (tin) gene has been considered as an important trait for wheat production in dryland environments. We used a spatial analysis approach with a daily time-step coupled radiation and transpiration efficiency model to simulate the impact of the reduced-tillering trait on wheat yield under different climate change scenarios across Australia's arable land. Our results show a small but consistent yield advantage of the reduced-tillering trait in the most water-limited environments both under current and likely future conditions. Our climate scenarios show that whilst elevated [CO2 ] (e[CO2 ]) alone might limit the area where the reduced-tillering trait is advantageous, the most likely climate scenario of e[CO2 ] combined with increased temperature and reduced rainfall consistently increased the area where restricted tillering has an advantage. Whilst long-term average yield advantages were small (ranged from 31 to 51 kg ha-1  year-1 ), across large dryland areas the value is large (potential cost-benefits ranged from Australian dollar 23 to 60 MIL/year). It seems therefore worthwhile to further explore this reduced-tillering trait in relation to a range of different environments and climates, because its benefits are likely to grow in future dry environments where wheat is grown around the world.

The effect of nitrogen-fertilizer and optimal plant population on the profitability of maize plots in the Wami River sub-basin, Tanzania: A bio-economic simulation approach.

Maize (Zea mays L.) is the essential staple in sub-Saharan Africa (SSA) and Tanzania in particular; the crop accounts for over 30% of the food production, 20% of the agricultural gross domestic product (GDP) and over 75% of the cereal consumption. Maize is grown under a higher risk of failure due to the over-dependence rain-fed farming system resulting in low income and food insecurity among maize-based farmers. However, many practices, including conservation agriculture, soil and water conservation, resilient crop varieties, and soil fertility management, are suggested to increase cereal productivity in Tanzania. Improving planting density, and the use of fertilizers are the immediate options recommended by Tanzania's government. In this paper, we evaluate the economic feasibility of the improved planting density (optimized plant population) and N-fertilizer crop management practices on maize net returns in semi-arid and sub-humid agro-ecological zones in the Wami River sub-Basin, Tanzania. We introduce a bio-economic simulation model using Monte Carlo simulation procedures to evaluate the economic viability of risky crop management practices so that the decision-maker can make better management decisions. The study utilizes maize yield data sets from two biophysical cropping system models, namely the APSIM and DSSAT. A total of 83 plots for the semi-arid and 85 plots for the sub-humid agro-ecological zones consisted of this analysis. The crop management practices under study comprise the application of 40 kg N-fertilizer/ha and plant population of 3.3 plants/m2 . The study finds that the use of improved plant population had the lowest annual net return with fertilizer application fetching the highest return. The two crop models demonstrated a zero probability of negative net returns for farms using fertilizer rates of 40 kg N/ha except for DSSAT, which observed a small probability (0.4%) in the sub-humid area. The optimized plant population presented 16.4% to 26.6% probability of negatives net returns for semi-arid and 14.6% to 30.2% probability of negative net returns for sub-humid zones. The results suggest that the application of fertilizer practices reduces the risks associated with the mean returns, but increasing the plant population has a high probability of economic failure, particularly in the sub-humid zone. Maize sub-sector in Tanzania is projected to continue experiencing a significant decrease in yields and net returns, but there is a high chance that it will be better-off if proper alternatives are employed. Similar studies are needed to explore the potential of interventions highlighted in the ACRP for better decision-making.

Modelling impact of early vigour on wheat yield in dryland regions.

Early vigour, or faster early leaf area development, has been considered an important trait for rainfed wheat in dryland regions such as Australia. However, early vigour is a genetically complex trait, and results from field experiments have been highly variable. Whether early vigour can lead to improved water use efficiency and crop yields is strongly dependent on climate and management conditions across the entire growing season. Here, we present a modelling framework for simulating the impact of early vigour on wheat growth and yield at eight sites representing the major climate types in Australia. On a typical soil with plant available water capacity (PAWC) of 147 mm, simulated yield increase with early vigour associated with larger seed size was on average 4% higher compared with normal vigour wheat. Early vigour through selection of doubled early leaf sizes could increase yield by 16%. Increase in yield was mainly from increase in biomass and grain number, and was reduced at sites with seasonal rainfall plus initial soil water <300 mm. Opportunities exists for development of early vigour wheat varieties for wetter sites. Soil PAWC could play a significant role in delivering the benefit of early vigour and would require particular attention.

Improving process-based crop models to better capture genotype×environment×management interactions.

In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al., 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework.

Impact of warming climate, sowing date, and cultivar shift on rice phenology across China during 1981-2010.

Climate change would exert significantly impact on crop yield by altering crop growth and development processes. Therefore, to ensure food security, it is necessary to assess the response and adaptation of crop phenology to the natural (mainly climate change) and artificial (including sowing date (SD) change and cultivar shift) factors. In this study, using field data from 113 agro-meteorological experiment stations across China, along with the Agricultural Production System Simulator (APSIM) oryza model, we investigated the trends of rice phenology in relation to climate change and agronomic factors (i.e., SD change and cultivar shift) from 1981 to 2010. We found that flowering date (FD) and maturity date (MD) of single-rice were delayed by 0.3 and 1.4 days 10a-1, respectively, but FD and MD of double-rice were advanced by 0.7-0.8 and 0.2-1.1 days 10a-1, respectively. Climate change advanced FD and MD of rice at representative stations except FD of late-rice, and shortened length of rice growth period. SD change of rice mainly affected duration of vegetative growth phase (VGP, from SD to FD), but had no significant impact on duration of reproductive growth phase (RGP, from FD to MD). Cultivar shift delayed FD and MD of rice at all representative stations except late-rice at Lianhua. Moreover, cultivar shift prolonged the duration of rice RGP by 0.2-2.8 days 10a-1. Overall, the results suggested that rice phenology was adapting to ongoing climate change by SD change and adoption of cultivars with longer RGP. Therefore, crop phenological characteristics should be sufficiently taken into account to develop adaptation strategies in the future.

Taking the climate risk out of transplanted and direct seeded rice: Insights from dynamic simulation in Eastern India.

Rice productivity in Eastern Indo-Gangetic plains (EIGP) is extremely low, in part due to the prevailing practice of cultivating long-duration transplanted rice under rainfed conditions which leads to water stress and significant yield losses in many seasons. Rice establishment alternatives such as direct seeded rice (DSR) require less water at planting but also are accompanied by climate risks that constrain adoption. For both conventional transplanted and DSR systems, successfully addressing climate-based production risks may provide a strong basis for sustainable rice intensification in EIGP. In this ex ante study of rice yield and yield variability, the APSIM cropping system model was used to evaluate the efficacy of risk-reducing management practices in both transplanted and DSR systems. Simulations were conducted with 44 years (1970-2013) of historical weather data from central Bihar, India. Results confirm that the prevailing farmer practice of transplanting long-duration cultivars under rainfed conditions (fTR) often results in delayed transplanting and the use of older seedlings, leading to low (median 1.6 t ha-1) and variable (Standard deviation (SD) 2.1 t ha-1) rice yields. To improve the fTR system, simulations suggest that adoption of medium-duration hybrid rice (3.2 t ha-1), provision of supplemental post-establishment irrigation (3.2 t ha-1), or transplanting appropriately aged seedlings (3.4 t ha -1) can double yields as single interventions while, in the case of supplemental irrigation, significantly reducing inter-annual production variability. Additional gains are achievable when interventions are layered: supplemental irrigation paired with medium-duration hybrids increased median rice yields to 4.6 t ha-1 with much lower variability (SD 1.0 t ha-1). In these improved systems where irrigation is used to transplant the crop, simulations revealed the importance of timely planting: high and stable yields are achievable for long-duration cultivars when transplanting is completed by 2 August with this window of opportunity extending to 16 August for medium-duration hybrids. In rainfed DSR systems, the potential pay-offs from single interventions were even higher with medium-duration hybrids resulting in a median yield of 4.5 t ha-1 against 1.8 t ha-1 with long-duration cultivars. For irrigated DSR systems, an optimum sowing window of early to mid-June was identified which resulted in higher and more stable yields with lower water requirements. Simulation results suggest several risk-reducing intensification pathways that can be selectively matched to farmer risk preferences and investment capabilities within the target region in EIGP.

Australian wheat production expected to decrease by the late 21st century.

Climate change threatens global wheat production and food security, including the wheat industry in Australia. Many studies have examined the impacts of changes in local climate on wheat yield per hectare, but there has been no assessment of changes in land area available for production due to changing climate. It is also unclear how total wheat production would change under future climate when autonomous adaptation options are adopted. We applied species distribution models to investigate future changes in areas climatically suitable for growing wheat in Australia. A crop model was used to assess wheat yield per hectare in these areas. Our results show that there is an overall tendency for a decrease in the areas suitable for growing wheat and a decline in the yield of the northeast Australian wheat belt. This results in reduced national wheat production although future climate change may benefit South Australia and Victoria. These projected outcomes infer that similar wheat-growing regions of the globe might also experience decreases in wheat production. Some cropping adaptation measures increase wheat yield per hectare and provide significant mitigation of the negative effects of climate change on national wheat production by 2041-2060. However, any positive effects will be insufficient to prevent a likely decline in production under a high CO2 emission scenario by 2081-2100 due to increasing losses in suitable wheat-growing areas. Therefore, additional adaptation strategies along with investment in wheat production are needed to maintain Australian agricultural production and enhance global food security. This scenario analysis provides a foundation towards understanding changes in Australia's wheat cropping systems, which will assist in developing adaptation strategies to mitigate climate change impacts on global wheat production.

Effectiveness of time of sowing and cultivar choice for managing climate change: wheat crop phenology and water use efficiency.

Climate change (CC) presents a challenge for the sustainable development of wheat production systems in Australia. This study aimed to (1) quantify the impact of future CC on wheat grain yield for the period centred on 2030 from the perspectives of wheat phenology, water use and water use efficiency (WUE) and (2) evaluate the effectiveness of changing sowing times and cultivars in response to the expected impacts of future CC on wheat grain yield. The daily outputs of CSIRO Conformal-Cubic Atmospheric Model for baseline and future periods were used by a stochastic weather generator to derive changes in mean climate and in climate variability and to construct local climate scenarios, which were then coupled with a wheat crop model to achieve the two research aims. We considered three locations in New South Wales, Australia, six times of sowing (TOS) and three bread wheat (Triticum aestivum L.) cultivars in this study. Simulation results show that in 2030 (1) for impact analysis, wheat phenological events are expected to occur earlier and crop water use is expected to decrease across all cases (the combination of three locations, six TOS and three cultivars), wheat grain yield would increase or decrease depending on locations and TOS; and WUE would increase in most of the cases; (2) for adaptation considerations, the combination of TOS and cultivars with the highest yield varied across locations. Wheat growers at different locations will require different strategies in managing the negative impacts or taking the opportunities of future CC.

Managing the risk of extreme climate events in Australian major wheat production systems.

Extreme climate events (ECEs) such as drought, frost risk and heat stress cause significant economic losses in Australia. The risk posed by ECEs in the wheat production systems of Australia could be better managed through the identification of safe flowering (SFW) and optimal time of sowing (TOS) windows. To address this issue, three locations (Narrabri, Roseworthy and Merredin), three cultivars (Suntop and Gregory for Narrabri, Mace for both Roseworthy and Merredin) and 20 TOS at 1-week intervals between 1 April and 12 August for the period from 1957 to 2007 were evaluated using the Agricultural Production System sIMulator (APSIM)-Wheat model. Simulation results show that (1) the average frequency of frost events decreased with TOS from 8 to 0 days (d) across the four cases (the combination of locations and cultivars), (2) the average frequency of heat stress events increased with TOS across all cases from 0 to 10 d, (3) soil moisture stress (SMS) increased with earlier TOS before reaching a plateau and then slightly decreasing for Suntop and Gregory at Narrabri and Mace at Roseworthy while SMS increased with TOS for Mace at Merredin from 0.1 to 0.8, (4) Mace at Merredin had the earliest and widest SFW (216-260) while Mace at Roseworthy had latest SFW (257-280), (5) frost risk and heat stress determine SFW at wetter sites (i.e. Narrabri and Roseworthy) while frost risk and SMS determine SFW at drier site (i.e. Merredin) and (6) the optimal TOS (window) to maximise wheat yield are 6-20 May, 13-27 May and 15 April at Narrabri, Roseworthy and Merredin, respectively. These findings provide important and specific information for wheat growers about the management of ECE risk on farm. Furthermore, the coupling of the APSIM crop models with state-of-the-art seasonal and intra-seasonal climate forecast information provides an important tool for improved management of the risk of ECEs in economically important cropping industries in the foreseeable future.

Responses to atmospheric CO2 concentrations in crop simulation models: a review of current simple and semicomplex representations and options for model development.

Elevated atmospheric CO2 concentrations ([CO2 ]) cause direct changes in crop physiological processes (e.g. photosynthesis and stomatal conductance). To represent these CO2 responses, commonly used crop simulation models have been amended, using simple and semicomplex representations of the processes involved. Yet, there is no standard approach to and often poor documentation of these developments. This study used a bottom-up approach (starting with the APSIM framework as case study) to evaluate modelled responses in a consortium of commonly used crop models and illuminate whether variation in responses reflects true uncertainty in our understanding compared to arbitrary choices of model developers. Diversity in simulated CO2 responses and limited validation were common among models, both within the APSIM framework and more generally. Whereas production responses show some consistency up to moderately high [CO2 ] (around 700 ppm), transpiration and stomatal responses vary more widely in nature and magnitude (e.g. a decrease in stomatal conductance varying between 35% and 90% among models was found for [CO2 ] doubling to 700 ppm). Most notably, nitrogen responses were found to be included in few crop models despite being commonly observed and critical for the simulation of photosynthetic acclimation, crop nutritional quality and carbon allocation. We suggest harmonization and consideration of more mechanistic concepts in particular subroutines, for example, for the simulation of N dynamics, as a way to improve our predictive understanding of CO2 responses and capture secondary processes. Intercomparison studies could assist in this aim, provided that they go beyond simple output comparison and explicitly identify the representations and assumptions that are causal for intermodel differences. Additionally, validation and proper documentation of the representation of CO2 responses within models should be prioritized.

Farming system context drives the value of deep wheat roots in semi-arid environments.

The capture of subsoil water by wheat roots can make a valuable contribution to grain yield on deep soils. More extensive root systems can capture more water, but leave the soil in a drier state, potentially limiting water availability to subsequent crops. To evaluate the importance of these legacy effects, a long-term simulation analysis at eight sites in the semi-arid environment of Australia compared the yield of standard wheat cultivars with cultivars that were (i) modified to have root systems which extract more water at depth and/or (ii) sown earlier to increase the duration of the vegetative period and hence rooting depth. We compared simulations with and without annual resetting of soil water to investigate the legacy effects of drier subsoils related to modified root systems. Simulated mean yield benefits from modified root systems declined from 0.1-0.6 t ha(-1) when annually reset, to 0-0.2 t ha(-1) in the continuous simulation due to a legacy of drier soils (mean 0-32mm) at subsequent crop sowing. For continuous simulations, predicted yield benefits of >0.2 t ha(-1) from more extensive root systems were rare (3-10% of years) at sites with shallow soils (<1.0 m), but occurred in 14-44% of years at sites with deeper soils (1.6-2.5 m). Earlier sowing had a larger impact than modified root systems on water uptake (14-31 vs 2-17mm) and mean yield increase (up to 0.7 vs 0-0.2 t ha(-1)) and the benefits occurred on deep and shallow soils and in more years (9-79 vs 3-44%). Increasing the proportion of crops in the sequence which dry the subsoil extensively has implications for the farming system productivity, and the crop sequence must be managed tactically to optimize overall system benefits.