*2.2. Measurements*

In the early phase of oat growth in BBCH-scale 11–12 (*german* "Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie"), the number of plants per 1 m<sup>2</sup> area was counted to assess mixtures density. Before harvesting, 20 plants were taken for detailed measurements, i.e., the number and weight of panicles, the number of grains, and the 1000 grains weight. Combine harvesting was performed with a plot harvester when oats were fully ripe (BBCH 97–99). After harvesting, grains, and straw of mixtures from the area 18 m2, were weighed. The final yields of grains per plot were converted into a notional humidity of 15%. For that reason, samples of grains (ca. 40 g.) and straw (ca. 40 g.) were dried at 105 ◦C using a forced-air oven until a constant weight was obtained. Based on the dry mass values, the grain yields were calculated [24]. Protein content (%) was determined using the InfraXact™ analyzer (Foss, Hillerod, Denmark) based on the near-infrared spectroscopy. The analysis was conducted in three technical replications per sample in the 570–1850 nm wavelengths. Each sample was scanned six times and compared with two internal standards (references) before calculating the mean value.

#### *2.3. Statistical Analysis of the Results*

The normality of distribution of the observed traits was tested with Shapiro–Wilk's normality test [25]. Next, the effects of the main factors under study (I factor–soil type: S.L. and H.C.; II factor–oat cultivars: 'Celer', 'Grajcar', 'Kasztan', 'Furman'; III factor–years: 2012, 2013, 2014) as well as all the interactions between them were estimated with a linear model for the three-way analysis of variance (ANOVA) for particular traits. The relationships between the traits were assessed based on Pearson's correlation coefficients and tested with the Tukey's test at *p* ≤ 0.05. The results were also analyzed with multivariate methods. The canonical variate analysis (CVA) was applied to present a multi-trait

assessment of similarity of the investigated treatments in a lower number of dimensions with the least possible loss of information [26]. This enabled graphic illustration of the variation in the traits of all treatments under analysis. The Mahalanobis distance was suggested to measure multi-trait treatments' similarity [27], whose significance was verified employing critical value *Dcr* known as the least significant distance [28]. Pearson's simple correlation coefficients were estimated to determine each original trait's relative share in the treatments' multivariate variation between values of the first two canonical variates and original individual traits. The GenStat v. 18 statistical software package was used for all the analyses.

The variation coefficient (V) was calculated to characterize the diversity of the sum of rainfall and temperature in the particular months of the growing season (April–August) 2012–2014.

$$V = \frac{S}{\overline{X}} \times 100\% \tag{1}$$

where:

*V*—the coefficient of variation,

*S*—a standard deviation,

*X*—arithmetic mean of the variable value.

#### *2.4. Weather Conditions*

The weather data were collected from the meteorological station in the Experimental Station in Mydlniki-Kraków (50◦05 N 19◦51 E). The weather conditions during the study period varied (Figures 1–3). The sums of precipitation (Figures 1 and 2) and the average daily air temperature (Figure 3) in 2012–2014 differed from the average for the long-term period (1951–2000). According to [29], the required amount of precipitation for oats during the vegetation period ranges from 270 mm on light (sandy) soils to 400 mm on heavy soils. The water demand for oats increases as the plant develops, reaching the highest values in June and then July. The critical period for water demands for oat in our study was in May 2012, which was very dry, according to the [30] classification. During that month, the amount of rainfall was only 23% of the long-term period. July 2012 was, according to the classification, average—76% of the long-term period and August 2012 was dry—67% of the long-term period. The total rainfall in these months was below the water demand of oat [29]. Based on the humidity characteristics in 2013, April, July, and August were very dry, May humid, and June too humid (213.1 mm of rainfall). In 2014, three out of five months of vegetation were classified as average (April, July and August), May as wet, and June as very dry (43.4 mm of rainfall).

Common vetch also has a high-water demand, especially during the flowering period. In the study period, the temperatures from sowing to harvest were higher than the average for the multi-year period 1951–2000, except for June 2014, when the average temperature was lower by 0.7 ◦C from the multi-year period. Based on the air temperature classification for Kraków [31], the months of January, March, April, and June 2012 were classified as warm. May, July, and August 2012 were hot. In 2013, January, February, April, and August were classified as regular. March 2013 was very cold, May and June were warm, and July was extremely warm. In 2014, May, June, and August were classified as regular months. April 2014 was warm, and March and July 2014 were extremely warm.

The variation coefficient (V) of the sum of precipitation in individual months of the vegetation period in 2012 was equal to 26%, proving the average variability of rainfall in that period. In 2013, the V was equivalent to 107%, which shows a substantial variability. In 2014, the V in individual months was 41%, which denotes a large variability of precipitation. Temperature variability in the respective months of vegetation period 2012–2014 was different. The V of temperature for the growing season 2012 was 70%, which denotes a large variability. In 2013, V = 28%, and in 2014, 25% indicated the average variability of temperature.

 **Figure 1.** Sum of precipitation (mm) in particular months of 2012–2014 and multiyear 1951–2000.

Months

**Figure 2.** Sum of precipitation (mm) in the vegetative period (April–August) and the years of study 2012–2014 compared to multiyear.

**Figure 3.** Mean temperatures (◦C) in the months of 2012–2014 and in multiyear 1951–2000.
