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1 15 ABSTRACT 16 Plant phenology, which describes the timing of plant development, is a major aspect of 17 plant response to environment and for crops, a major determinant of yield. Many studies have 18 focused on comparing model equations for describing how phenology responds to climate but 19 the effect of crop model calibration, also important for determining model performance, has 20 received much less attention. The objectives here were to obtain a rigorous evaluation of 21 prediction capability of wheat phenology models, to analyze the role of calibration and to 22 document the various calibration approaches. The 27 participants in this multi-model study 23were provided experimental data for calibration and asked to submit predictions for sites and 24 years not represented in those data. Participants were instructed to use and document their 25 "usual" calibration approach. Overall, the models provided quite good predictions of 26 phenology (median of mean absolute error of 6.1 days) and did much better than simply using 27 the average of observed values as predictor. The results suggest that calibration can 28 compensate to some extent for different model formulations, specifically for differences in 29 simulated time to emergence and differences in the choice of input variables. Conversely, 30 different calibration approaches were associated with major differences in prediction error 31 between the same models used by different groups. Given the large diversity of calibration 32 approaches and the importance of calibration, there is a clear need for guidelines and tools to 33 aid with calibration. Arguably the most important and difficult choice for calibration is the 34 choice of parameters to estimate. Several recommendations for calibration practices are 35 proposed. Model applications, including model studies of climate change impact, should 36 focus more on the data used for calibration and on the calibration methods employed. 37

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%.

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The implementation of conservation agriculture (CA) remains crucial for facing interannual variability in climatic conditions that impact durum wheat production and food security. The current work was conducted to assess the effects of the tillage practice, previous crop, and nitrogen (N) fertilization rate on the agronomic and economic performances of rainfed durum wheat in a semi-arid environment in Tunisia. Tillage practices included no-tillage (NT) and conventional tillage (CT). Preceding crops were either a common vetch or a bread wheat. The N rates applied were: 0, 75, 100, 120, and 140 kg N ha−1. Our results show that, based on a 2-year experiment, tillage practices are not affecting grain yield, grain N, and gross margins. However, the N-use efficiency of durum wheat was significantly higher when wheat was grown using NT. Grain yield and N content in grain were 340 kg ha−1 and 0.34%; much higher after vetch than after bread wheat. For both tillage practices, the merit of 75 kg N ha−1 is paramount to maximize yield through a more efficient use of available N. Our results highlight the importance of no-tillage-based CA combined with rotation, including vetch, on enhanced yields, N-use efficiency, and gross margins. These findings provide the evidence of the positive impact of CA for rainfed durum wheat under semi-arid Mediterranean conditions.