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Context Within-field yield variability affects crop production and management decisions. To understand and manage this variability, different techniques have been deployed to measure and monitor the crops (and soils) at various spatial scales, including manual measurements, harvester-mounted yield monitors, proximal and remote sensing and crop simulation modelling. The value of this increasing data availability to enhance process understanding and on-ground management is unclear. Objective This study aimed to investigate the value of the increasingly available spatial data from different sources to understand important soil-plant processes amenable to improvement in both simulation modelling and for better management decisions for dryland cropping. Methods We collected three types of measurement data (manual sampling, sensed data from satellite and drone, and yield maps) over a 10 ha field and conducted simulations using the process-based soil-plant model APSIM at different spatial scales (varied from 1 m2 up to 10 ha). We assessed the agreement between ground measurements and yield maps, analysed the potential to use remotely sensed vegetation indices to estimate yield, and the scale at which process-based modelling could be reliable. Results Wheat yield extracted from yield map at 1 m2 spatial resolution only explained 30% of the variation in yield measured from 1 m2 manual sampling, with better agreement when data was aggregated to 1 ha strip-scale (R2 = 0.66, NRMSE = 9.1%). Remotely sensed vegetation indices (VI) were better correlated with the yield map when aggregating images to coarse spatial resolution (> 50 m × 50 m), while high-resolution drone VI increased the correlation at finer scales. However, the relationship and the timing of the highest correlation differed between years. APSIM simulated point-based yield measured from manual samples with NRMSE of 19.4%, but it was difficult to capture spatial variation, Context: Within-field yield variability affects crop production and management decisions. To understand and manage this variability, different techniques have been deployed to measure and monitor the crops (and soils) at various spatial scales, including manual measurements, harvester-mounted yield monitors, proximal and remote sensing and crop simulation modelling. The value of this increasing data availability to enhance process understanding and on-ground management is unclear. Objective: This study aimed to investigate the value of the increasingly available spatial data from different sources to understand important soil-plant processes amenable to improvement in both simulation modelling and for better management decisions for dryland cropping. Methods: We collected three types of measurement data (manual sampling, sensed data from satellite and drone, and yield maps) over a 10 ha field and conducted simulations using the process-based soil-plant model APSIM at different spatial scales (varied from 1 m2 up to 10 ha). We assessed the agreement between ground measurements and yield maps, analysed the potential to use remotely sensed vegetation indices to estimate yield, and the scale at which process-based modelling could be reliable. Results: Wheat yield extracted from yield map at 1 m2 spatial resolution only explained 30% of the variation in yield measured from 1 m2 manual sampling, with better agreement when data was aggregated to 1 ha strip-scale (R2 = 0.66, NRMSE = 9.1%). Remotely sensed vegetation indices (VI) were better correlated with the yield map when aggregating images to coarse spatial resolution (> 50 m × 50 m), while high-resolution drone VI increased the correlation at finer scales. However, the relationship and the timing of the highest correlation differed between years. APSIM simulated point-based yield measured from manual samples with NRMSE of 19.4%, but it was difficult to capture spatial v

The relative time to maturity of grain crops is an important consideration for producers, yet there are no universally accepted classification schemes for cultivar phenology to guide decisions on variety selection and time of sowing. A first edition of an industry guide for wheat variety maturity was recently developed for use across Australia, representing a significant step forward for the grains industry. The aim of this paper was to revise and extend this industry guide to make it more robust, agronomically functional and meaningful to industry. The Australian Cereal Phenology Classification (ACPC) presented herein was developed using an unprecedented phenological data set with a diverse array of genotypes, environments and management. Field experiments were carried out with 70 wheat and 30 barley cultivars at 15 sites across Australia between 2017 and 2020. Thermal time to anthesis data were used to rank cultivars according to their relative phenology and divide them into classes, and then boundary cultivars of both species were selected to separate these classes. The resulting classification scheme divides wheat and barley into phenology classes ranging from ‘quick’ to ‘mid’ to ‘slow’. New cultivars to market can be assigned a phenology classification based on their thermal time to anthesis relative to the boundary cultivars. The ACPC will help growers, agronomists, breeders and researchers make informed decisions regarding cultivar comparison and selection while reducing misclassification and confusion across regions. The same methodology used to derive and validate the ACPC can be applied internationally to standardise descriptions of crop phenology.

The relative time to maturity of grain crops is an important consideration for producers, yet there are no universally accepted classification schemes for cultivar phenology to guide decisions on variety selection and time of sowing. A first edition of an industry guide for wheat variety maturity was recently developed for use across Australia, representing a significant step forward for the grains industry. The aim of this paper was to revise and extend this industry guide to make it more robust, agronomically functional and meaningful to industry. The Australian Cereal Phenology Classification (ACPC) presented herein was developed using an unprecedented phenological data set with a diverse array of genotypes, environments and management. Field experiments were carried out with 70 wheat and 30 barley cultivars at 15 sites across Australia between 2017 and 2020. Thermal time to anthesis data were used to rank cultivars according to their relative phenology and divide them into classes, and then boundary cultivars of both species were selected to separate these classes. The resulting classification scheme divides wheat and barley into phenology classes ranging from ‘quick’ to ‘mid’ to ‘slow’. New cultivars to market can be assigned a phenology classification based on their thermal time to anthesis relative to the boundary cultivars. The ACPC will help growers, agronomists, breeders and researchers make informed decisions regarding cultivar comparison and selection while reducing misclassification and confusion across regions. The same methodology used to derive and validate the ACPC can be applied internationally to standardise descriptions of crop phenology.

The relative time to maturity of grain crops is an important consideration for producers, yet there are no universally accepted classification schemes for cultivar phenology to guide decisions on variety selection and time of sowing. A first edition of an industry guide for wheat variety maturity was recently developed for use across Australia, representing a significant step forward for the grains industry. The aim of this paper was to revise and extend this industry guide to make it more robust, agronomically functional and meaningful to industry. The Australian Cereal Phenology Classification (ACPC) presented herein was developed using an unprecedented phenological data set with a diverse array of genotypes, environments and management. Field experiments were carried out with 70 wheat and 30 barley cultivars at 15 sites across Australia between 2017 and 2020. Thermal time to anthesis data were used to rank cultivars according to their relative phenology and divide them into classes, and then boundary cultivars of both species were selected to separate these classes. The resulting classification scheme divides wheat and barley into phenology classes ranging from ‘quick’ to ‘mid’ to ‘slow’. New cultivars to market can be assigned a phenology classification based on their thermal time to anthesis relative to the boundary cultivars. The ACPC will help growers, agronomists, breeders and researchers make informed decisions regarding cultivar comparison and selection while reducing misclassification and confusion across regions. The same methodology used to derive and validate the ACPC can be applied internationally to standardise descriptions of crop phenology.

The relative time to maturity of grain crops is an important consideration for producers, yet there are no universally accepted classification schemes for cultivar phenology to guide decisions on variety selection and time of sowing. A first edition of an industry guide for wheat variety maturity was recently developed for use across Australia, representing a significant step forward for the grains industry. The aim of this paper was to revise and extend this industry guide to make it more robust, agronomically functional and meaningful to industry. The Australian Cereal Phenology Classification (ACPC) presented herein was developed using an unprecedented phenological data set with a diverse array of genotypes, environments and management. Field experiments were carried out with 70 wheat and 30 barley cultivars at 15 sites across Australia between 2017 and 2020. Thermal time to anthesis data were used to rank cultivars according to their relative phenology and divide them into classes, and then boundary cultivars of both species were selected to separate these classes. The resulting classification scheme divides wheat and barley into phenology classes ranging from ‘quick’ to ‘mid’ to ‘slow’. New cultivars to market can be assigned a phenology classification based on their thermal time to anthesis relative to the boundary cultivars. The ACPC will help growers, agronomists, breeders and researchers make informed decisions regarding cultivar comparison and selection while reducing misclassification and confusion across regions. The same methodology used to derive and validate the ACPC can be applied internationally to standardise descriptions of crop phenology.

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (http://doi.org/10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.

Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

Phosphorus (P) increasingly constrains crop productivity in Australian Vertosols (Australian Soil Classification, Isbell et al., 2021). However, P behaviour and dynamics in Vertosols are still not well understood especially within complex cropping systems. To complement our knowledge in P dynamics in Vertosols and collect critical information as a baseline for future agricultural model development, nine Vertosols were incubated with various P fertilizer treatments. Phosphorus dynamics were modelled in relation to physical and chemical properties. Results indicate that physical and chemical properties and P dynamics of the Vertosols are highly variable and differ substantially from the other soil types. This suggests that inclusion of selected soil properties to current soil P models could help to improve the understanding of the fate of applied P in Vertosols. This will be critical for future development of sustainable P fertilizer management strategies.

The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models. The R code and formatted data used for the statistical analyses are included.

The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models. The R code and formatted data used for the statistical analyses are included. (2022-02-11).

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

The dataset contains 6 growing seasons of a local winter wheat cultivar ‘Wakanui’ at two farms located in the Canterbury Region of New Zealand. The data of the experiment was used in the AgMIP-Wheat Phase 4 project to evaluate the performance of an ensemble of 29 crop models to predict the effect of changing sowing dates and rates on yield and yield components, in a high-yielding environment. The treatments were managed for non-stress conditions. Data include local daily weather, soil characteristics and initial soil N conditions, crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, and yield components), and cultivar information. Simulations include both daily in-season and end-of-season results from 29 wheat crop models.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of

Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development

Intercropping of two or more crop species increases the efficiency of resource use and often produces a greater yield per unit land area. The relative efficiency of intercropping depends on row configuration, but there is a shortage of modelling-based evaluation of alternative intercropping options due to the inadequacy of standard process-based crop models to simulate resource capture, growth and yield formation when the canopy is spatially structured in strips. We implemented a light interception model for strip crops into the APSIM Classic model and combined it with a quasi-Bayesian approach to derive the model parameters to simulate crop growth and grain yield in maize-soybean strip intercropping. We used 4 years of field data for 5 different row configurations to derive key model parameters for simulation of light interception, LAI dynamics, biomass growth and grain yield of maize and soybean intercrops. Key model parameters (e.g. RUE, k etc.) were found to change with row-spacing and configuration, posing challenges to simulate different configurations with a single parameter set. The potential ranges of these key parameters were derived by constraining the model to observed data. The model can be potentially used to evaluate impact of planting configurations on productivity of strip intercropping systems, but the variability of key model parameters among configuration treatments calls for further in-depth research to improve modelling physiology of strip intercrops.

Functional–structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible.We used an existing FSP model of avocado (Persea americana ‘Hass’) and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization.POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns.Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also

Functional–structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible.We used an existing FSP model of avocado (Persea americana ‘Hass’) and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization.POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns.Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also

The GRDC national phenology initiative has been established to better predict flowering time of wheat and barley across the major cropping regions of Australia. A total of 64 wheat and 32 barley genotypes, selected for their diverse phenology, are being genotyped and phenotyped under different controlled conditions to parameterise the model. The same genotypes are also being grown and phenotyped at five field sites (two × WA, SA, NSW, VIC) across eight times of sowing to validate the model. The initiative will deliver a web-based tool through the National Variety Trials (NVT) website where growers and advisors can plan cultivar × time of sowing decisions for their specific environments. This will become available in 2022, with new cultivars being added as they are released.

Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is ther

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is ther