Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO 2 , as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO 2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO 2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO 2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts.
Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multimodeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.
57Predicting wheat phenology is important for cultivar selection, for effective crop 58 management and provides a baseline for evaluating the effects of global change. Evaluating 59 how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat 60 modeling groups participated in this evaluation. Model predictions depend not only on model 61 structure but also on the parameter values. This study is thus an evaluation of modeling groups, 62 which choose the structure and fix or estimate the parameters, rather than an evaluation just of 63 model structures. Our target population was wheat fields in the major wheat growing regions 64 of Australia under current climatic conditions and with current local management practices. 65The environments used for calibration and for evaluation were both sampled from this same 66 target population. The calibration and evaluation environments had neither sites nor years in 67 common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology 68 for new sites and weather conditions. Mean absolute error (MAE) for the evaluation 69 environments, averaged over predictions of three phenological stages and over modeling 70 groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group 71 mean and median had prediction errors nearly as small as the best modeling group. For a given 72 modeling group, MAE for the evaluation environments was significantly correlated with MAE 73 for the calibration environments, which suggests that it would be of interest to test ensemble 74 predictors that weight individual modeling groups based on performance for the calibration 75 data. About two thirds of the modeling groups performed better than a simple but relevant 76 benchmark, which predicts phenology by assuming a constant temperature sum for each 77 development stage. The added complexity of crop models beyond just the effect of temperature 78 was thus justified in most cases. Finally, there was substantial variability between modeling 79 groups using the same model structure, which implies that model improvement could be 80 4 achieved not only by improving model structure, but also by improving parameter values, and 81 in particular by improving calibration techniques. 82 uncertainty 84 85 5 A second aspect of evaluation that must be specified is the modeling group or groups 131 that are being evaluated. We reserve the term "model" specifically for model structure, i.e. the 132 model equations, while modeling group determines both the model structure and the parameter 133 values, which are chosen or estimated by the group running the model. It is clear that predictions 134
A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology as a function of soil, weather, and management is important. Mechanistic crop models are a major tool for such predictions. It has been shown that there is a large variability between predictions by different modeling groups for the same inputs, and therefore, a need for shared improvement of crop models. Two pathways to improvement are through improved understanding of the mechanisms of the modeled system, and through improved model parameterization. This article focuses on improving crop model parameters through improved calibration, specifically for prediction of crop phenology. A detailed calibration protocol is proposed, which covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. For those aspects where knowledge of the model and target environments is required, the protocol gives detailed guidelines rather than strict instructions. The protocol includes documentation tables, to make the calibration process more transparent. The protocol was applied by 19 modeling groups to three data sets for wheat phenology. All groups first calibrated their model using their "usual" calibration approach. Evaluation was based on data from sites and years not represented in the training data. Compared to usual calibration, l calibration following the new protocol significantly reduced the error in predictions for the evaluation data, and reduced the variability between modeling groups by 22%.
As the basis of livestock feeding and related performances, pastures evolution and dynamics need to be carefully monitored and assessed, particularly in the Alps where the effects of land abandonment are further amplified by climate change. As such, increases in temperature associated with changes in precipitation patterns and quantity are leading to modifications of grassland extent and composition with consequences on the pastoral systems. This study applied a machine learning approach (Random Forest) and GIS techniques to map the suitability of seven pasture macro types most representative of the Italian Alps and simulated the impact of climate change on their dynamics according to two future scenarios (RCP4.5, 8.5), two time-slices (2011–2040, 2041–2070), and three RCMs (Aladin, CMCC, ICTP). Results indicated that (i) the methodology was robust to map the current suitability of pasture macro types (mean accuracy classification = 98.7%), so as to predict the expected alterations due to climate change; (ii) future climate will likely reduce current extend of suitable pasture (−30% on average) and composition, especially for most niche ecosystems (i.e., pastures dominated by Carex firma and Festuca gr. Rubra); (iii) areas suited to hardier but less palatable pastures (i.e., dominated by Nardus stricta and xeric species) will expand over the Alps in the near future. These impacts will likely determine risks for biodiversity loss and decreases of pastoral values for livestock feeding, both pivotal aspects for maintaining the viability and profitability of the Alpine pastoral system as a whole.
The impact of climate change on the agricultural systems of three major islands in the Mediterranean basin, namely Sicily, Crete and Cyprus, was evaluated using a suite of specifically calibrated crop models and the outputs of a regional circulation model for Representative Concentration Pathway (RCP) 4.5 and 8.5 downscaled to 12 km of resolution and tested for its effectiveness in reproducing the local meteorological data. The most important annual (wheat, barley, tomato and potato) and perennial (grapevine and olive tree) crops were selected to represent the agricultural systems of the islands. The same modelling framework was used to test the effectiveness of autonomous adaptation options, such as shifting sowing date and the use of varieties with different growing season length. The results highlighted that, on average, warmer temperatures advanced both anthesis and maturity of the selected crops, but at different magnitudes depending on the crop and the island. Winter crops (barley, wheat and potato) experienced the lowest impact in terms of yield loss with respect to the baseline, with even some positive effects, especially in Sicily where both wheat and barley showed a general increase of 9% as compared to the baseline, while potato increased up to + 17%. Amongst perennial crops, olive tree showed low variation under RCP 4.5, but on average increased by 7% under RCP 8.5 on the three islands. Climate change had a detrimental effect specifically on tomato (− 2% on average in RCP 8.5 and 4.5 on the three islands) and grapevine (− 7%). The use of different sowing dates, or different varieties, revealed that for winter crops early autumn sowing is still the best option for producing wheat and barley in future periods on the three islands under both future scenarios. For tomato and potato, advancing sowing date to early winter is a winning strategy that may even increase final yield (+ 9% for tomato and + 17% for potato, on average). For grapevine, the use of late varieties, while suffering the most from increasing temperatures and reduced rainfall (− 15%, on average), is still a valuable option to keep high yield levels with respect to earlier varieties, which even if showing some increases with respect to the baseline have a generally much lower production level. The same may be applied to olive tree although the production differences between late and early varieties are less evident and climate change exerts a favourable influence (+ 4 and + 3% for early and late varieties, respectively).
Durum wheat is one of the most important crops in the Mediterranean basin. The choice of the cultivar and the sowing time are key management practices that ensure high yield. Crop simulation models could be used to investigate the genotype × environment × sowing window (G × E×SW) interactions in order to optimize farmers' actions. The aim of this study was to evaluate the performance of the wheat model SiriusQuality in simulating durum wheat yields in Mediterranean environments and its potential to explore the G × E×SW interactions. SiriusQuality was assessed in multiple growing seasons at seven sites located in Italy, Spain and Morocco, where locally adapted cultivars were grown. The model showed good ability in predicting anthesis and maturity date (Pearson r >0.8), as well as above ground biomass and grain yield (6 % < nRMSE < 18 %). The model was then used to find the optimal 30-day sowing window to maximize grain yields at four sites, two were located in Italy (Florence, Foggia), and the other two were in Spain (Santaella) and Morocco (Sidi El Aydi) respectively. Among the cultivars, on the average between all sowing window, Amilcar had the best performance in Foggia (+33 % compared to the traditional cultivar Simeto) and in Sidi El Aydi (+22 % compared to Karim), Karim in Florence (+19 % compared to Creso) and in Santaella (+6 % compared to Amilcar). Instead Creso and Simeto showed the lowest production at all locations. The results showed that an earlier sowing window compared to the traditional one would have a positive effect on wheat yields in all environments tested, because of increased maximum leaf area index, grain number and size, and grain filling duration. Moreover, with earlier sowing, grain filling coincides with higher soil water availability, reducing the water stress and increasing the accumulation of dry mass in grains. In cooler and wetter locations, cultivars characterized by higher leaf area index and radiation use efficiency had the higher number of grains, while in the hottest and driest locations, short-cycle cultivars with high grain dry matter potential (e.g. through enhanced "stay green" capacity) should be preferred.
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