Core Ideas Leaf area in chia cannot be accurately predicted by the product of leaf width and length. Regressing leaf area log linearly on width and length accounts for change of shape with size. We provide accurate prediction models valid across experiments, populations, and N levels. Mixed‐model meta‐regression allows integrating leaf area data across experiments. Leaf area (LA) is an important agronomic trait but is difficult to measure directly. It is therefore of interest to estimate LA indirectly using easily measured correlated traits. The most commonly used approach to predict LA uses the product of leaf width (LW) and leaf length (LL) as single predictor variable. However, this approach is insufficient to estimate LA of chia (Salvia hispanica L.) because the leaves are differently shaped depending on leaf size. The objectives of this study were to develop a nondestructive LA estimation model for chia using LW and LL measurements that can take differences in leaf shape into account and to determine whether population and nitrogen fertilizer level have an effect on the accuracy of a LA estimation model. A total of 840 leaves were collected from five different field experiments in 2015 and 2016 conducted in southwestern Germany. The experiments comprised eight populations of black‐ and white‐seeded chia (07015 ARG, 06815 BOL, 06915 ARG, G8, G7, G3, W13.1, and Sahi Alba 914) and three nitrogen fertilizer levels (0, 20, and 40 kg N ha−1). We used meta‐regression to integrate the data accounting for heterogeneity between experiments, populations, and nitrogen levels. The proposed LA estimation model with two measured predictor variables (LL and LW) was LA = 0.740 × LL0.866 × LW1.075 and provided the highest accuracy across populations and nitrogen levels [r = 0.989; mean squared deviation (MSD) = 2.944 cm4]. The model LA = 1.396 × LW1.806 with only LW was almost as accurate (r = 0.977; MSD = 5.831 cm4).

Core Ideas Bivariate linear mixed models should be used to estimate correlations between traits in designed experiments. The fourth sowing date resulted in highest seed yield and harvest index with high PUFA/SFA ratio and content of PUFA and protein. Under later sowing, protein content and saturated fatty acids increased whereas oil content and PUFA decreased. Sowing chia in Egypt is recommended between the middle and end of September to achieve higher yields and good quality. Chia (Salvia hispanica L.) has recently been rediscovered as functional “superfood” for human nutrition. Chia is a short‐day plant and it naturally grows in tropical and subtropical environments. It can cope with water stress and thus could also be cultivated in arid regions. The aim of this study was to determine the suitable sowing date (SWD) for chia in Egypt. Therefore, the effect of six different sowing dates (August to October) on agronomic traits like seed yield (SY), plant height, seed yield per plant (SYP), harvest index (HI) and quality traits such as protein, oil, mucilage content, and fatty acid profile was evaluated. The last SWD resulted in a significantly lower SY (125.91 kg ha−1), HI (0.11), oil content (27.08%), content of polyunsaturated fatty acids (PUFA) (81.46%), and ratio of PUFA to saturated fatty acids (7.24), but higher thousand kernel weight (TKW) (1.51 g), protein content (26.03%), and higher content of saturated fatty acids (SFA) (8.21%) compared with the other SWDs. The maximum observed SY (664.94 kg ha−1) was recorded for SWD 4 (3 Oct. 2015). In this study, the thermal time at onset of flowering and the corresponding prevailing daylength showed a strong positive relationship for daylengths higher than 10.4 h that corresponded to about 600°C d (between SWD 5 and 6). Considering the obtained results and the possible risk of high temperature stress for very early sowings (SWD 1 and 2), sowing dates between middle and end of September are recommended to achieve a marketable seed quality and higher yields.

Chia (Salvia hispanica L.) seeds are becoming increasingly popular as a superfood in Europe. However, broad experience in growing chia in temperate climates is missing. Crop simulation models can be helpful tools for management and decision-making in crop production systems in different regions. The objective of this study was to adapt the CROPGRO model for simulating growth and yield of chia. Data sets from a field experiment conducted over 2 yr in southwestern Germany (48 • 74′ N, 08 • 92′ E, 475 m above sea level) were used for model adaptation. The initial starting point was the CROPGRO-soybean [Glycine max (L.) Merr.] model as a template for parameterizing temperature functions and setting tissue composition. Considerable iterations were made in optimizing growth, development, and photosynthesis parameters. After model calibration, the simulation of leaf area index (LAI) was reasonable for both years, slightly over-predicting LAI with an average d-statistic of 0.95 and root mean square error (RMSE) of 0.53. Simulations of final leaf number were close to the observed data with dstatistic of 0.98 and RMSE of 1.36. Simulations were acceptable for total biomass (d-statistic of 0.93), leaf (d-statistic of 0.94), stem (d-statistic of 0.94), pod mass (d-statistic of 0.89), and seed yield (d-statistic of 0.88). Pod harvest index (HI) showed good model performance (d-statistic of 0.96 and RMSE of 0.08). Overall, the model adaptation resulted in a preliminarily adapted model with realistically simulated crop growth variables. Researchers can use the developed chia model to extend knowledge on the eco-physiology of chia and to improve its production and adaption to other regions. Abbreviations: CSM, cropping system model; CUL, cultivar; DSSAT, Decision Support System for Agrotechnology Transfer; ECO, ecotype; HI, harvest index; LAI, leaf area index; N1, nitrogen fertilizer treatment 0 kg ha −1 ; N2, nitrogen fertilizer treatment 20 kg ha −1 ; N3, nitrogen fertilizer treatment 40 kg ha −1 ; PD, photothermal days; PHI, pod harvest index; RMSE, root mean square error; SLA, specific leaf area; SPE, species; Tb, base temperature; TD, thermal days; Tmax, maximum temperature; Tmin, minimum temperature; Topt, optimum temperature. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.