**1. Introduction**

Climate-smart agriculture (CSA) strives for sustainable productivity, quality and economic viability while leaving a minimal foot print on the environment [1,2]. Despite the growing need for food and feed raw materials, crop yield is only one factor of the portfolio of the desired plant performances [3]. Plant genotypes developed on conventional tillage may not necessarily adapt to the changed cropping environment and new, specifically adapted genotypes may need to be developed [4]. To promote sustainability and biodiversity conservation, there is a growing interest in some old wheat species as well. Ruiz et al. [5] described some yield-related traits that have been identified as potential targets to achieve better grain yields of old wheat varieties in no-tillage and minimum tillage systems. Special attention is directed to the possible production of alternative cereals in organic production [6]. These species are nowadays rather produced for feed as alternatives to oats and barley. Ancient wheat genotypes that have the ability to maintain green leaf area ('stay green' traits) throughout grain filling are potential candidates for adapting and improving wheat for higher yield in arid and semi-arid regions. 'Stay green' is a vital characteristic associated with the capacity of the plant to maintain CO2 assimilation and photosynthesis [7]. Because of the more frequent and more severe extreme weather conditions, the 'stay green' characteristic is especially important for breeders in producing more drought and/or heat tolerant crop species.

Spelt wheat (*Triticum aestivum* ssp. spelta L.), the oldest known wheat species cultivated in ancient Egypt and Italy, was as a result of spontaneous crossings of wild grasses. Reviving of spelt wheat production has started in the hilly and mountainous region of Central Europe and North America at the end of the 20th century [8]. It is an alternative crop, growing without any special soil related and climatic demands [9]. Spelt has the potential for low input production and adaptation to harsh ecological conditions and resistance to diseases [10]. Owing to its hulled grain and genetic polymorphism of its population, spelt is resistant to pests and diseases and hence suitable for organic production [6]. Spelt wheat and its products could serve as an abundant source of protein and a grea<sup>t</sup> proportion of soluble fibre emerging in the final spelt wheat products [11].

The identification of those factors which are determining the adaptation and nitrogen (N) utilization of spelt wheat is important for the successful introduction of the crop to a new environment in the comparison of non-fertilized and fertilized (100 kgN ha−1) circumstances [12]. Several studies compared the productivity of spelt and common wheat in particular years. Most of them reported substantially higher yield of common wheat. The difference in yield often was as grea<sup>t</sup> as 60% in favour of common wheat [13] comparing low (6.8 kg ha−1) and high (33.8 kg ha−1) phosphorus supply. In the study of Jablonskyté-Rašˇcé et al. [14] the average common wheat yield was 28% higher than that of the spelt wheat using ecological fertilizers. Budzynski et al. [15] reported 2.55 t ha−<sup>1</sup> higher yield potential average of common wheat than spelt in response to N rates. Some studies though reported that spelt was able to produce similar amount of yields as the common wheat (e.g., [16]). Probably because of the fact that climatic conditions of particular years, notably the climate × fertilisation interactions could significantly influence the grain yield of winter wheat [17]. However, there is still a lack of knowledge of the competitiveness of spelt grown at extensive or medium fertilisation levels.

Based on the results of Lazauskas et al. [18] we may assume that under low or moderate fertilisation inputs nitrogen will remain a major limiting factor for realizing high winter wheat yields in the coming decades. Nitrogen fertilisation directly or indirectly influences the LAI (leaf area index), degree of soil coverage by plants, leaf chlorophyll content, and other biophysical parameters, that can be characterized by vegetation indices, such as NDVI (normalized difference vegetation index) or SPAD (strongly correlated to chlorophyll content). Vegetation indices can be used as indicators of crop growth [19], nutrient status [20], and yield development [21]. Yield forecasting on the basis of vegetation indices acquired in the early stages of development can help farmers to make decisions about irrigation or additional fertilisation demand [22]. Normalized difference vegetation index have been widely used in agricultural remote sensing applications [22]. Leaf chlorophyll content (indexed e.g., by SPAD value) can be used as an accurate plant N status indicator. SPAD allows precise N fertilizer requirement calculations that are fundamental for enhancing N uptake efficiency [23,24]. A number of studies investigated the leaf growth of common wheat (e.g., [25,26]), but there are only a few data available regarding LAI changes of spelt wheat.

In addition for grain crops, harvest index (HI), the ratio of harvested grain to aboveground biomass, could be used as a measure of reproductive efficiency [27]. Although the effect of agronomical factors on HI of winter wheat was studied in a large number of works, there are just a few similar data for spelt wheat.

Because of the large inter-annual variability it is important to monitor the yield formation process of cereals in various years. More extensive data on yield formation of different wheat species may assist the spreading of production of alternative, even healthier cereals. The aim of this study is to provide a detailed analysis of vegetative, generative and spectral properties of spelt and common wheat grown under different N (from zero to moderate) levels.

### **2. Materials and Methods**

The effect of nitrogen fertilisation on the yield and vitality parameters under various common and spelt winter wheat varieties was studied in parallel experiments in a split-plot design in four replications. The experiments were carried out in the years 2015/2016, 2017/2018 and 2018/2019 at the Agricultural Institute of the Centre for Agricultural Research in Martonvásár (47◦30 N, 18◦82 E). The experiment was suspended for the 2016/2017 growing season, because of technical reasons. The N

fertiliser doses (always applied in the form of ammonium-nitrate) were 0, 40, 80, 120 kg ha−<sup>1</sup> (designated as N0, N40, N80 and N120, respectively) in the main plots. The same dose (120 kg ha−1) of phosphorus (P) and potassium (K) were given to every plot each year. Conventional tillage (no ploughing, only disk and cultivator use) was applied in the 0–20 cm soil layer after the PK fertilisation. By-products were always left on the field and incorporated in the soil. N fertiliser was applied in two splits: one-third before sowing (with PK) and two-third in early spring at tillering. Three genotypes of common wheat, Mv Kolo, Mv Marsall and Mv Kokárda, and spelt wheat, Mv Martongold, Franckenkorn and Mv Vitalgold, were sown in plots. All the genotypes except Franckenkorn (German origin) were breeded at Martonvásár. Around 9 m<sup>2</sup> (1.44 × 6 m) plots were used for each (N-level × variety) treatment. The chernozem soil of the experiment is non acidic loam with deep A horizon (Table 1).


**Table 1.** Main physical and chemical properties of the experimental plot at different layers at Martonvásár(Hungary)in2018.

Owing to its favourable hydraulic properties (water holding capacity is 0.2 cm<sup>3</sup> cm<sup>−</sup>3) and high soil organic matter content, based on the EU-SHG European Soil Database [28], the experiment site belongs to one of the most fertile regions of Central Europe.

Data of monthly precipitation and air temperature were recorded at the meteorological station at Martonvásár (Figure 1). The total amount of precipitation in the vegetative period (October–June) was ~30% lower in 2018/2019 (350 mm) than in the other two years (475 mm in 2015/2016 and 495 mm in 2017/2018) and ~16% lower than the 30 years' average (419 mm). The distribution of precipitation was less favourable for wheat owing to a prolonged dry period in March and April in 2015/2016 and 2018/2019, but the drought was compensated by high amount of precipitation (139 mm) in May 2019 (around flowering). The mean temperature during the vegetative period was similar during the three experimental years (8.6 ◦C in 2015/2016; 8.9 ◦C in 2017/2018 and 8.8 ◦C in 2018/2019) but considerably higher than the 30 years' average (7.3 ◦C). On the other hand, the course of the spring temperature was considerably different across the years especially in 2018 when the relatively cold February– March period (4 ◦C colder than the other two years) was followed by a relatively (3.5–4.5 ◦C) warmer April–May period.

Planting took place on 17 October 2015, 26 October 2017, and 17 October 2018 and the plots were harvested in the first decade of July in each year. Grain yield was estimated from the harvested plot yields and were converted to tons per hectare. Harvest index was estimated from plant samples of 0.5-m long sections taken before harvest.

LAI was measured by a non-destructive method using AccuPAR ceptometer [29] at flowering stage. Eight measurements were made below the canopy, four parallel and four across to the rows in each plot. The parallel and perpendicular measurements were averaged. The maximum LAI (LAImax) values were measured in the third decade of May in each year.

**Figure 1.** Monthly mean temperature and total precipitation at Martonvásár (Hungary) during the cropping seasons of 2015/2016, 2017/2018 and 2018/2019.

The chlorophyll content of the flag leaves at flowering was determined by using Minolta Chlorophyll Meter, SPAD-502 [30]. The measurements were made at the middle of the leaf lamina of 20 flag leaves. The SPAD values were converted to total chlorophyll values by using the conversion equation of Zhu et al. 2012 [31]. The 20 measurement results of each plot were averaged, and the mean values were used in the statistical analysis. NDVI was measured with a Trimble GreenSeeker® handheld crop sensor [32]. The measurements were made at flowering in sunny weather ~80 cm above the crop canopy. Two measurements per plot were carried out. The two readings in each plot were averaged, and the mean values were used in the statistical analysis.

The performance of spelt and common wheat, the effects of the different N fertiliser levels as well as the performance of the different varieties were evaluated with paired sample *t*-tests [33]. A difference was regarded to be significant in case the corresponding *t*-test resulted in a smaller than 0.05 probability (*p*) value.

Based on crop vitality indices (LAImax, SPAD and NDVI) as independent variables, a multivariable linear yield estimation equation (model) was constructed (1) for both wheat species. This Equation (1) can be applied for yield (Y) forecast using data already available around flowering.

$$\text{Y} = \text{a} + \text{b} \times \text{LAI}\_{\text{max}} + \text{c} \times \text{SPAD} + \text{d} \times \text{NDVI} \tag{1}$$

where a, b, c, d are fitting parameters, that were determined with regression analysis using the lm function of the stats v.3.6.1 R package [34].

From the 144 observed data record (Table A1 in the Appendix A) of the three years a random subset of 114 records were selected for determining/calibrating the parameters of the estimation equations (2). The remaining 30 records were used for validating the model. Estimated (Ye) and observed (Yo) yield data were compared using simple statistical indicators: Coefficient of determination (R2) and mean absolute error (MAE), where the *mean* function calculates the arithmetic average of the arguments and *n* denotes the number of the estimated-observed data pairs.

$$R^2 = \frac{\left(\sum\_{i=1}^n \left(\boldsymbol{\chi^i\_o - \text{mean}\left(\boldsymbol{\chi^i\_o\right)}\right)\left(\boldsymbol{\chi^i\_e - \text{mean}\left(\boldsymbol{\chi^i\_e\right)}\right)\right)^2}{\sum\_{i=1}^n \left(\boldsymbol{\chi^i\_o - \text{mean}\left(\boldsymbol{\chi^i\_o\right)}\right)^2 \sum\_{i=1}^n \left(\boldsymbol{\chi^i\_e - \text{mean}\left(\boldsymbol{\chi^i\_e\right)}\right)^2}}\right)^2} \tag{2}$$
 
$$\begin{array}{l} MAE = \sum\_{i=1}^n \frac{|\boldsymbol{\chi^e\_o - \text{mean}\left(\boldsymbol{\chi^e\_e}\right)|}{n}}{n} \end{array} \tag{2}$$

### **3. Results and Discussion**

All the observed data are presented in Table A1 in the Appendix A and summarized in Figures 2 and A1 in the Appendix A.
