A trait-based model ensemble approach to design rice plant types for future climate.

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait-based multi-model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi-model simulations targeting enhanced productivity, and aggregated results into model-ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait-parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP-Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid-century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context-specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait-based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

Inherent trait differences explain wheat cultivar responses to climate factor interactions: New insights for more robust crop modelling.

Climate change predictions foresee a combination of rising CO2 , temperature and altered precipitation. Effects of single climatic variables are well defined, but the importance of combined variables and genotypic effects is less known, although pivotal for assessing climate change impacts, for example, with crop growth models. This study provides developmental and physiological data from combined climatic factors for two distinct wheat cultivars (Paragon and Gladius), as a basis to improve predictions for climate change scenarios. The two cultivars were grown in controlled climate chambers in a fully factorial setup of atmospheric CO2 concentration, growth temperature and watering regime. The cultivars differed considerably in their developmental rate, response pattern and the parameters responsible for most of their variation. The growth of Paragon was linked to climatic effects on photosynthesis and mainly affected by temperature. Paragon was overall more negatively affected by all treatment combinations compared to Gladius. Gladius was mostly affected by watering regime. The cultivars' acclimation strategies to climate factors varied significantly. Thus, considering a single factor is an oversimplification very likely impacting the accuracy of crop growth models. Intraspecific crop variation could help understanding genotype by environment variation. Cultivars with high phenotypic plasticity may have greater resilience against climatic variability.

Models for simulating the frequency of pests and diseases of Coffea arabica L.

We developed models for simulating trends over time as functions of the thermal index and models for estimating the levels of infestation of the coffee leaf miner and coffee berry borer and the severity of disease for coffee leaf rust and cercospora, the main phytosanitary problems in coffee crops around the world. We used historical series of climatic data and levels of pest infestation and disease severity in Coffea arabica for high and low yields for seven locations in the two main coffee-producing regions in the state of Minas Gerais in Brazil, Sul de Minas Gerais and Cerrado Mineiro. We conducted two analyses: (a) we simulated the trends of the progress of diseases and pests over time using non-linear models. We only used the thermal index because air temperature is commonly measured by farmers in the regions. (b) We estimated the levels of pest infestation and disease severity using multiple linear regression, with the levels of diseases and pests as dependent variables and accumulated degree days (DD), coffee foliage (LF) estimated by DD and the number of nodes (NN) estimated by DD as independent variables. We used DD and LF = f (DD) and NN = f (DD) to predict diseases and pests with accuracy. MAPEs were 19.6, 5.7, 9.5, and 15.8% for rust, cercospora, leaf miner, and berry borer, respectively, for Sul de Minas Gerais. Establishing phytosanitary alerts using only air temperature was possible with these models.

Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models.

Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (Lactuca sativa L. var. capitata). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm-2), edible biomass (RMSE = 6.87 g m-2), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76-68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios.

Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields.

We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water deficit conditions. Using 213 randomly selected genotypes as the training set, 90 single nucleotide polymorphism (SNP) loci were identified using a genome-wide association study (GWAS), explaining 42-77% of crop model parameter variation. SNP-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNP-based model accounted for 37% (control) and 29% (water deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNP-based crop model was advantageous when simulating yields under either control or water stress conditions in an independent season. Crop model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water deficit environments. Crop models have the potential to use single-environment information for predicting phenotypes under different environments.

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.

Raising genetic yield potential in high productive countries: Designing wheat ideotypes under climate change.

Designing crop ideotype is an important step to raise genetic yield potential in a target environment. In the present study, we designed wheat ideotypes based on the state-of-the-art knowledge in crop physiology to increase genetic yield potential for the 2050-climate, as projected by the HadGEM2 global climate model for the RCP8.5 emission scenario, in two high-wheat-productive countries, viz. the United Kingdom (UK) and New Zealand (NZ). Wheat ideotypes were optimized to maximize yield potential for both water-limited (IW2050 ) and potential (IP2050 ) conditions by using Sirius model and exploring the full range of cultivar parameters. On average, a 43-51% greater yield potential over the present winter wheat cv. Claire was achieved for IW2050 in the UK and NZ, whereas a 51-62% increase was obtained for IP2050 . Yield benefits due to the potential condition over water-limitation were small in the UK, but 13% in NZ. The yield potentials of wheat were 16% (2.6 t ha-1) and 31% (5 t ha-1) greater in NZ than in the UK under 2050-climate in water-limited and potential conditions respectively. Modelling predicts the possibility of substantial increase in genetic yield potential of winter wheat under climate change in high productive countries. Wheat ideotypes optimized for future climate could provide plant scientists and breeders with a road map for selection of the target traits and their optimal combinations for wheat improvement and genetic adaptation to raise the yield potential.

Can the development of drought tolerant ideotype sustain Australian chickpea yield?

Terminal drought is a major problem in many areas where chickpea is grown on stored soil moisture. This is exacerbated by the lack of a targeted breeding approach focusing on key traits contributing to yield formation under water-limited conditions. There is no study to develop a chickpea ideotype and test it against commercial varieties under various management systems across the Australian grain belt. This study proposed a chickpea ideotype that can be grown in water deficit areas and compared its performance with commercial chickpea genotypes across the Australian grain belt. Important traits for ideotype construction and breeding were identified and tested against selected commercial varieties in silico in the Australian grain belt using the APSIM crop model. The key phenological, morphological and physiological traits were determined in the field at the University of Sydney's IA Watson Grains Research Centre near Narrabri for ideotype targeting. Five commercial chickpea genotypes (Sonali, PBA Hattrick, Kyabra, Tyson and Amethyst) were selected for evaluation against the chickpea ideotype. The constructed chickpea ideotype showed 76% resemblance to Sonali which performed well under water limited conditions. Simulated yield ranged from 760 to 3902 kg/ha across the Australian grain belt, with consistently higher yield in the ideotype compared with the commercial cultivars. The growing environments were grouped into three major clusters using the soil water deficit method with varying water stress levels. It is evident that grain filling is the most critical stage where soil moisture deficit caused chickpea yield losses up to 16.5% in the present study. By incorporating key target traits and targeting the right environment, chickpea yields can be sustained in the Australian grain belt or in an area having similar agro-ecological characteristics.

Can increased leaf photosynthesis be converted into higher crop mass production? A simulation study for rice using the crop model GECROS.

Various genetic engineering routes to enhance C3 leaf photosynthesis have been proposed to improve crop productivity. However, their potential contribution to crop productivity needs to be assessed under realistic field conditions. Using 31 year weather data, we ran the crop model GECROS for rice in tropical, subtropical, and temperate environments, to evaluate the following routes: (1) improving mesophyll conductance (gm); (2) improving Rubisco specificity (Sc/o); (3) improving both gm and Sc/o; (4) introducing C4 biochemistry; (5) introducing C4 Kranz anatomy that effectively minimizes CO2 leakage; (6) engineering the complete C4 mechanism; (7) engineering cyanobacterial bicarbonate transporters; (8) engineering a more elaborate cyanobacterial CO2-concentrating mechanism (CCM) with the carboxysome in the chloroplast; and (9) a mechanism that combines the low ATP cost of the cyanobacterial CCM and the high photosynthetic capacity per unit leaf nitrogen. All routes improved crop mass production, but benefits from Routes 1, 2, and 7 were ≤10%. Benefits were higher in the presence than in the absence of drought, and under the present climate than for the climate predicted for 2050. Simulated crop mass differences resulted not only from the increased canopy photosynthesis competence but also from changes in traits such as light interception and crop senescence. The route combinations gave larger effects than the sum of the effects of the single routes, but only Route 9 could bring an advantage of ≥50% under any environmental conditions. To supercharge crop productivity, exploring a combination of routes in improving the CCM, photosynthetic capacity, and quantum efficiency is required.

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.

Modelling climate change impacts on viticultural yield, phenology and stress conditions in Europe.

Viticulture is a key socio-economic sector in Europe. Owing to the strong sensitivity of grapevines to atmospheric factors, climate change may represent an important challenge for this sector. This study analyses viticultural suitability, yield, phenology, and water and nitrogen stress indices in Europe, for present climates (1980-2005) and future (2041-2070) climate change scenarios (RCP4.5 and 8.5). The STICS crop model is coupled with climate, soil and terrain databases, also taking into account CO2 physiological effects, and simulations are validated against observational data sets. A clear agreement between simulated and observed phenology, leaf area index, yield and water and nitrogen stress indices, including the spatial differences throughout Europe, is shown. The projected changes highlight an extension of the climatic suitability for grapevines up to 55°N, which may represent the emergence of new winemaking regions. Despite strong regional heterogeneity, mean phenological timings (budburst, flowering, veraison and harvest) are projected to undergo significant advancements (e.g. budburst/harvest can be >1 month earlier), with implications also in the corresponding phenophase intervals. Enhanced dryness throughout Europe is also projected, with severe water stress over several regions in southern regions (e.g. southern Iberia and Italy), locally reducing yield and leaf area. Increased atmospheric CO2 partially offsets dryness effects, promoting yield and leaf area index increases in central/northern Europe. Future biomass changes may lead to modifications in nitrogen demands, with higher stress in northern/central Europe and weaker stress in southern Europe. These findings are critical decision support systems for stakeholders from the European winemaking sector.

Frost trends and their estimated impact on yield in the Australian wheatbelt.

Radiant spring frosts occurring during reproductive developmental stages can result in catastrophic yield loss for wheat producers. To better understand the spatial and temporal variability of frost, the occurrence and impact of frost events on rain-fed wheat production was estimated across the Australian wheatbelt for 1957-2013 using a 0.05 ° gridded weather data set. Simulated yield outcomes at 60 key locations were compared with those for virtual genotypes with different levels of frost tolerance. Over the last six decades, more frost events, later last frost day, and a significant increase in frost impact on yield were found in certain regions of the Australian wheatbelt, in particular in the South-East and West. Increasing trends in frost-related yield losses were simulated in regions where no significant trend of frost occurrence was observed, due to higher mean temperatures accelerating crop development and causing sensitive post-heading stages to occur earlier, during the frost risk period. Simulations indicated that with frost-tolerant lines the mean national yield could be improved by up to 20% through (i) reduced frost damage (~10% improvement) and (ii) the ability to use earlier sowing dates (adding a further 10% improvement). In the simulations, genotypes with an improved frost tolerance to temperatures 1 °C lower than the current 0 °C reference provided substantial benefit in most cropping regions, while greater tolerance (to 3 °C lower temperatures) brought further benefits in the East. The results indicate that breeding for improved reproductive frost tolerance should remain a priority for the Australian wheat industry, despite warming climates.

Identifying plant traits to increase wheat yield under irrigated conditions.

Crop models have frequently been used to identify desired plant traits for rainfed wheat (Triticum aestivum L.). However, efforts to apply these models to irrigated wheat grown under non-limiting water and nitrogen conditions have been rare. Using simulation models to identify plant traits that impact yield can facilitate more targeted cultivar improvement and reduce time and cost. In this study, the SSM-iCrop model was employed to identify effective plant traits for increasing the yield of irrigated wheat in four distinct environments in Iran. A comprehensive range of traits related to phenology, leaf area development, dry matter production, and yield formation, which exhibited reported genetic variation, were tested. The impact of these traits on yield showed slight variation across different environmental zones due to genetic × environment interaction. However, across all environments, modifying current cultivars to increase radiation use efficiency (RUE) resulted in a 19 % increase in yield, accelerating leaf area development led to a 10 %-15 % increase, lengthening the grain filling period resulted in a 14 % increase, and extending the vegetative period led to a 6 % increase. These improvements were all statistically significant. Considering that longer duration cultivars may disrupt cropping systems and the need to develop simple methods for targeting and phenotyping RUE, faster leaf area development was found as the most promising option to increase irrigated wheat yield under optimal water and nitrogen management within a short time frame. It should be noted that cultivars with modified traits needed higher water and nitrogen inputs to support increased yields. These findings can be applied to select desirable key traits for targeted breeding and expedite the production of high-yielding cultivars of irrigated wheat in various environmental zones. The potential for further improvement through combined traits requires further investigation.

Evaluating the impact of weather forecasts on productivity and environmental footprint of irrigated maize production systems.

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

A harmonised dataset for modelling select underutilised crops Across EU.

Whilst simulating crop performance in different environments can help fill the knowledge gap and improve the adoption of crops that are currently neglected and underutilised in conventional agrifood systems, lack of experimental data remains a barrier to widespread modelling of these crops. To date, no attempt has been made to collate sub-species crop data that are specifically suited for modelling underutilised crops. This article describes the first attempt to develop a database for crop modelling data with a focus on European underutilised crops. Following a pilot study to identify crops with the potential across the EU, a structured dataset of detailed experimental data was developed by analysing more than 500 agronomic studies that were published across European agroclimatic zones from 1972 to 2022. The dataset contains minimum information for calibrating basic crop models for any location in the EU provided that enough experimental and environmental data are available. More specifically, the database includes crop phenology, yield, management practices, geographic and pedo-climatic details of select underutilised and neglected species. The information underwent a curation procedure to ensure its quality. The collated database will be used in CropBASE, the global knowledge base for underutilised crops.

Field and in-silico analysis of harvest index variability in maize silage.

Maize silage is a key component of feed rations in dairy systems due to its high forage and grain yield, water use efficiency, and energy content. However, maize silage nutritive value can be compromised by in-season changes during crop development due to changes in plant partitioning between grain and other biomass fractions. The partitioning to grain (harvest index, HI) is affected by the interactions between genotype (G) × environment (E) × management (M). Thus, modelling tools could assist in accurately predicting changes during the in-season crop partitioning and composition and, from these, the HI of maize silage. Our objectives were to (i) identify the main drivers of grain yield and HI variability, (ii) calibrate the Agricultural Production Systems Simulator (APSIM) to estimate crop growth, development, and plant partitioning using detailed experimental field data, and (iii) explore the main sources of HI variance in a wide range of G × E × M combinations. Nitrogen (N) rates, sowing date, harvest date, plant density, irrigation rates, and genotype data were used from four field experiments to assess the main drivers of HI variability and to calibrate the maize crop module in APSIM. Then, the model was run for a complete range of G × E × M combinations across 50 years. Experimental data demonstrated that the main drivers of observed HI variability were genotype and water status. The model accurately simulated phenology [leaf number and canopy green cover; Concordance Correlation Coefficient (CCC)=0.79-0.97, and Root Mean Square Percentage Error (RMSPE)=13%] and crop growth (total aboveground biomass, grain + cob, leaf, and stover weight; CCC=0.86-0.94 and RMSPE=23-39%). In addition, for HI, CCC was high (0.78) with an RMSPE of 12%. The long-term scenario analysis exercise showed that genotype and N rate contributed to 44% and 36% of the HI variance. Our study demonstrated that APSIM is a suitable tool to estimate maize HI as one potential proxy of silage quality. The calibrated APSIM model can now be used to compare the inter-annual variability of HI for maize forage crops based on G × E × M interactions. Therefore, the model provides new knowledge to (potentially) improve maize silage nutritive value and aid genotype selection and harvest timing decision-making.

Enhancing water use efficiency and grain yield of wheat by optimizing irrigation supply in arid and semi-arid regions of Pakistan.

The lack of good irrigation practices and policy reforms in Pakistan triggers major threats to the water and food security of the country. In the future, irrigation will happen under the scarcity of water, as inadequate irrigation water becomes the requirement rather than the exception. The precise application of water with irrigation management is therefore needed. This research evaluated the wheat grain yield and water use efficiency (WUE) under limited irrigation practices in arid and semi-arid regions of Pakistan. DSSAT was used to simulate yield and assess alternative irrigation scheduling based on different levels of irrigation starting from the actual irrigation level up to 65% less irrigation. The findings demonstrated that different levels of irrigation had substantial effects on wheat grain yield and total water consumption. After comparing the different irrigation levels, the high amount of actual irrigation level in semi-arid sites decreased the WUE and wheat grain yield. However, the arid site (Site-1) showed the highest wheat grain yield 2394 kg ha-1 and WUE 5.9 kg-3 on actual irrigation (T1), and with the reduction of water, wheat grain yield decreased continuously. The optimal irrigation level was attained on semi-arid (site-2) with 50% (T11) less water where the wheat grain yield and WUE were 1925 kg ha-1 and 4.47 kg-3 respectively. The best irrigation level was acquired with 40% less water (T9) on semi-arid (site-3), where wheat grain yield and WUE were 1925 kg ha-1 and 4.57 kg-3, respectively. The results demonstrated that reducing the irrigation levels could promote the growth of wheat, resulting in an improved WUE. In crux, significant potential for further improving the efficiency of agricultural water usage in the region relies on effective soil moisture management and efficient use of water.

Identifying and modelling key physiological traits that confer tolerance or sensitivity to ozone in winter wheat.

Tropospheric ozone threatens crop production in many parts of the world, especially in highly populated countries in economic transition. Crop models suggest substantial global yield losses for wheat, but typically such models fail to address differences in ozone responses between tolerant and sensitive genotypes. Therefore, the purpose of this study was to identify physiological traits contributing to yield losses or yield stability under ozone stress in 18 contrasting wheat cultivars that had been pre-selected from a larger wheat population with known ozone tolerance. Plants were exposed to season-long ozone fumigation in open-top chambers at an average ozone concentration of 70 ppb with three additional acute ozone episodes of around 150 ppb. Compared to control conditions, average yield loss was 18.7 percent, but large genotypic variation was observed ranging from 2.7 to 44.6 percent. Foliar chlorophyll content represented by normalized difference vegetation index and net CO2 assimilation rate of young leaves during grain filling were the physiological traits most strongly correlated with grain yield losses or stability. Accumulative effects of chronic ozone exposure on photosynthesis were more detrimental for grain yield than instantaneous effects of acute ozone shocks, or accelerated senescence of older leaves represented by changes in the ratio of brown leaf area/green leaf area index. We used experimental data of two selected tolerant or sensitive varieties, respectively, to parametrize the LINTULCC2 crop model expanded with an ozone response routine. By specifying parameters representing the distinct physiological responses of contrasting genotypes, we simulated yield losses of 7 percent (tolerant) or 33 percent (sensitive). By considering genotypic differences in ozone response models, this study helps to improve the accuracy of simulation studies, estimate the effects of adaptive breeding, and identify physiological traits for the breeding of ozone tolerant wheat varieties that could deliver stable yields despite ozone exposure.

Performance Prediction of Durum Wheat Genotypes in Response to Drought and Heat in Climate Change Conditions.

With an approach combining crop modelling and biotechnology to assess the performance of three durum wheat cultivars (Creso, Duilio, Simeto) in a climate change context, weather and agronomic datasets over the period 1973-2004 from two sites, Benatzu and Ussana (Southern Sardinia, Itay), were used and the model responses were interpreted considering the role of DREB genes in the genotype performance with a focus on drought conditions. The CERES-Wheat crop model was calibrated and validated for grain yield, earliness and kernel weight. Forty-eight synthetic scenarios were used: 6 scenarios with increasing maximum air temperature; 6 scenarios with decreasing rainfall; 36 scenarios combining increasing temperature and decreasing rainfall. The simulated effects on yields, anthesis and kernel weights resulted in yield reduction, increasing kernel weight, and shortened growth duration in both sites. Creso (late cultivar) was the most sensitive to simulated climate conditions. Simeto and Duilio (early cultivars) showed lower simulated yield reductions and a larger anticipation of anthesis date. Observed data showed the same responses for the three cultivars in both sites. The CERES-Wheat model proved to be effective in representing reality and can be used in crop breeding programs with a molecular approach aiming at developing molecular markers for the resistance to drought stress.

Adequacy of nitrogen-based indicators for assessment of cropping system performance: A modelling study of Danish scenarios.

The EU nitrogen expert panel (EUNEP) has proposed nitrogen-based indicators for farm productivity (N output), efficiency (NUE) and environmental emissions (N surplus). This model-based study (using the Daisy model) was carried out, i) to study the effects of soil type, soil organic matter (SOM), cropping pre-histories varying in C input, 3-to-4 manure-to-mineral N proportions and ten crop rotations on the N-based indicators, and ii) to evaluate the adequacy of these indicators by establishing quantitative relationships between N surplus, N loss and soil organic N (SON) stock change. The results, averaged over 24-year simulation period, indicated that grass-clover dominant rotations had highest N output and showed a tendency to increase SON stocks when compared with spring-cereal monocultures. For most rotations, the NUE ranged between 70 and 75 %. The SON stocks were mainly influenced by initial SOM and cropping prehistory, and stocks increased only under low initial SOM and low C input cropping pre-history (spring barley). Overall, SON stocks tended to increase under low C input pre-history, coarse sand, low initial SOM and high manure N, however, this combination did not result in highest productivity, NUE, and lowest N losses. The relations between N surplus, N loss and SON stock change were strongly affected by crop rotations, emphasizing that using N surplus as an indicator for N leaching/losses while ignoring changes in SON stocks may result in biased conclusions, e.g. estimated average error for N losses ranged from -45 % (underestimation) for maize monoculture to +50 % (overestimation) for continuous grass-clover ley. The results also imply that the environmental assessment of cropping systems must be improved by combining above indicators with estimation of N loss and SON stock changes. This study provides a detailed account of N balance components/N indicators for diverse crop rotations and their use according to the recommendations of the EUNEP.