Relationship between spring triticale physiological traits and productivity changes as affected by different N rates

ABSTRACT

There is still a lack of knowledge about the physiological traits of spring triticale (x Triticosecale Wittm.) and their relationship with grain yield and protein content under the conditions of the environmental Zone Nemoral 2. The objective of this study was to determine the relationships among the physiological indices, grain yield and protein content as affected by nitrogen (N) rates. The correlation among leaf area index (LAI), chlorophyll index (SPAD), canopy greenness index (CGI), leaf area duration (LAD) and grain yield as well as direct and indirect effects of those traits on the yield were investigated using a path analysis. Grain yield, protein content and physiological indices were significantly (P ≤ .01) affected by N fertilization. N₉₀ level was the best compromise for the yield and physiological indices. The interaction of all physiological indices influenced the grain yield by 27–39%, protein by 42–44%. SPAD and LAI had greater influence on grain yield and grain protein than CGI and LAD. SPAD had positive direct dominant (the highest) effect on the yield only at BBCH 59 and BBCH 69 (50% of the tested cases). LAI was responsible for 19–39% of the correlation between yield and physiological indices. The physiological indices can be used for spring triticale growth modelling and agronomic management for improved productivity and grain quality. SPAD and LAI values, established at BBCH 45–69, can be used for grain yield prediction and those estimated at BBCH 69 can be used for grain protein prediction.

Abbreviations BBCH: Biologische Bundesantalt, Bundessortenamt und Chemische Industrie (decimal system for a uniform coding of phenologically similar growth stages of all mono- and dicotyledonous plant species); CGI: canopy greenness index; GS: growth stage; LAD: leaf area duration; LAI: leaf area index; SPAD: chlorophyll index (soil plant analysis development)

KEYWORDS:

• Grain yield
• physiological indices
• protein
• path analysis
• Triticosecale wittm

Introduction

Interest in triticale, now a well-established crop internationally, has developed around two areas of potential use for grain and forage. In recent years, triticale has been receiving increasing attention as a potential energy crop for bioethanol production (McKenzie et al. 2014). Due to the climate change impact on crop production, there is an urgent need for more tolerant crops able to withstand adverse climatic conditions and which would increase diversification of crops in the farming systems (Eudes 2015). Triticale is environmentally more flexible than other cereals and is capable of producing much higher grain and biomass yield than other cereals (Roques et al. 2016). Spring triticale cultivars are generally later maturing than wheat, which limits production in short growing season countries (Eudes 2015). Over the last 20 years, there has been noticed a 50% growth in triticale productivity (Eudes 2015). In 2010, 14.5 million tons of triticale grain were harvested in 28 countries across the world (FAOSTAT 2012). Improving photosynthesis and its adaptation to environmental conditions is one of the agronomic goals (Gonzalez et al. 2010).

Leaf area index (LAI) is a good indicator of crop state and is closely linked to other crop and soil variables such as biomass, grain yield, nutrition status and crop nitrogen uptake. As a result, the use of LAI data to derive information on these variables is highly correct (Casa et al. 2012). Reduction of LAI may cause a decrease in the amount of photosynthetic active radiation intercepted by the foliage and leaf photosynthesis (Cai et al. 2011). Consequently, high LAI of crops is associated with high-yield, higher plant photosynthetic rate, high dry matter production and is also a key factor determining high crop yield (Ronga et al. 2015). LAI variation was found to be influenced by tillage and crop residues (Kulig et al. 2010; Zhao et al. 2012) and eco-agrosystems (Ronga et al., 2015). Previous studies have shown that agronomic and physiological traits such as LAI, leaf area duration (LAD) and SPAD (chlorophyll index) are closely linked with high grain yield of various crops, including wheat (Triticum aestivum L.) (Poblaciones et al. 2009; Li et al. 2012; Wang et al. 2014), rice (Oryza sativa L.) (Li et al. 2012) and spring barley (Hordeum vulgare L.) (Janušauskaitė & Auškalnienė 2014). However, there is little published data on the impact of physiological traits on grain yield and grain protein of spring triticale. The contribution of these traits to yield may change with crops, varieties, cropping systems, fertilization level, years and meteorological factors (Janušauskaitė 2009, 2014; Kulig et al. 2010; Cammarano et al. 2011; Ronga et al. 2015). Chlorophyll content can be successfully used to study stress physiology and abiotic stresses, including nutrient deficiencies (Kalaji et al. 2016).

While numerous investigations on the canopy structure (LAI, LAD) and plant nutrition status (SPAD, canopy greenness index (CGI)) have been conducted in many crops, the published data on their use in spring triticale are very scarce. There remains a paucity of evidence on the physiological traits of spring triticale and their relationship with grain yield and quality during the growing season under the conditions of the biogeographical region of Europe environmental Zone Nemoral 2 (Metzger et al. 2012). The climate is continental there. The growing season lasts for on average 196 days (190–204), the sum of temperatures above +10° is on average 2717°C (2561–2898°C). The physiological traits are a helpful tool for predicting grain yield and grain quality in a similar climate zone.

Nitrogen is an essential nutrient for plant growth, development and productivity (Saleem et al. 2010). The response of grain yield of triticale to N nutrition has been reported (Lestingi et al. 2010; Janušauskaitė 2014). The effect of N on physiological traits, associated with triticale productivity, has not been extensively studied yet. To fill this gap, the objectives of the study were: (i) to determine the effect of nitrogen (N) fertilization levels on the physiological indices of spring triticale (x Triticosecale Wittm.); (ii) for the first time, to explore the relationship between spring triticale physiological traits such as SPAD, CCD, LAI, LAD and grain yield and protein content as affected by different N levels at different growth stages; and (iii) to explain the causality of the associations among the physiological traits, grain yield and protein content through direct and indirect effects. The novelty of this work is that the study revealed the relationship between spring triticale physiological indicators and productivity not only through correlation method, but also through path analysis, which gave a more comprehensive picture than simple correlations.

Materials and methods

Experimental site and soils

Four field experiments were conducted at the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (55⁰23′50″ N and 23⁰51′40″ E, elevation – 45 m) across the years 2008–2011. The soil of the experimental site is Endocalcari-Endohypogleyic Cambisol. The mean soil characteristics (at 0–25 cm sampling depth) of the experimental plots, determined annually at the beginning of the experiment, were as follows: available P_A–L 43–73 mg kg⁻¹, available K_A–L 110–123 mg kg⁻¹, pH_KCl 5.5–6.7. The content of was 1.3–2.9 mg kg⁻¹ and was 4.1–7.3 mg kg⁻¹ in 0–40 cm soil layer ( – ionometrically, – spectrophotometrically).

Treatments and agronomic management

Spring triticale (cultivar Nilex) was sown at a density of 400 viable seeds per m², following spring barley (Hordeum vulgare L.). For all treatments, 66 kg ha⁻¹ P (as granular monocalcium phosphate Ca(H₂PO₄)₂H₂O) and 130 kg ha⁻¹ K (as KCl) were applied pre-sowing. Nitrogen (N) was applied at the rates of 60, 90, 120, 150 and 180 kg N ha⁻¹ as ammonium nitrate (NH₄NO₃) and without fertilizer N supply as a control treatment. The experiment was laid out in a complete randomized block design with four replications. The plot size was 20.4 m² (2.1 m × 9.7 m). Weed control, diseases and pest management were carried out in accordance with the crop development as required.

Each year, the plots were harvested within the first 10-day period of August at complete maturity (BBCH 89) with a plot harvester ‘Wintersteiger Delta’ (Germany). Grain yield was adjusted to 15% moisture content. Grain protein content was measured using an Infratec 1241 grain analyser.

Measurements of physiological indices

Chlorophyll index (SPAD, soil plant analysis development) was measured using a chlorophyll meter Minolta SPAD 502 (Minolta Camera Co. Ltd., Osaka, Japan). The measurements were made in the middle part of the fully expanded, randomly selected flag leaves of 10 plants per plot (40 plants per each treatment) from 1.00 to 4.00 pm (local time) four times on clear days.

LAI (m⁻²m⁻²) measurements of spring triticale canopy were done using a computer image analysis system WinFOLIA2004a. Each measurement was done on five plants per experimental plot. Green leaves were removed and measured. Using a value of plant density derived from counting plants in 1 m⁻², LAI was computed as the ratio between leaf area and the corresponding land area (LAI, expressed as m⁻² of leaves m⁻² of soil). In each experimental year, four measurements of SPAD and LAI were performed at the following growth stages (GS): stem elongation (BBCH 31), booting (BBCH 45), end of heading GS (BBCH 59) and end of flowering (BBCH 69) (Meier, 2001). CGI was calculated as the product of the LAI and the SPAD readings (Cammarano et al. 2011):(1) LAD was calculated using the following formula:(2) where L₁ and L₂ are the first and second measurements of green leaf area, and T₁ and T₂ represent the time of the first and second measurements.

The weather conditions

Rainfall differed markedly between years. The growing season of 2008 was dry with a total amount of rainfall as low as 170 mm, or 68% of the long-term mean. The growing seasons in 2009 and 2011 were wetter with a total rainfall of 298 mm and 262 mm, respectively, or by 19% and 4% higher than the long-term mean. The 2010 growing season was the wettest with the rainfall totalling 390 mm, which was 56% higher compared to the long-term mean. The mean air temperature in 2008 and 2009 was similar to the long-term mean. In 2010 and 2011, the mean air temperature during the growing season was 0.7–1.4°C, 0.6–2.4°C, 3.0–4.0°C and 0.6–3.1°C above the long-term mean in May, June, July and August, respectively.

Statistical analysis

A two-way analysis of variance was performed. The combined data of grain yield, protein and physiological indices were analysed by a simple correlation, multiple linear regression and path analysis. A path analysis was made on the basis of the correlation coefficients taking grain yield and protein as effect and the remaining estimated physiological characters as cause. Path analysis differentiates between correlation and causation by partitioning simple correlation coefficients between the independent variables (physiological indices investigated) and dependent variable (grain yield and protein content in the grain) into direct and indirect effects. The sum of the entire path shows the strength of different variables on dependent indices (Y). Path analysis results were determined according to the example of the following equation (Snedecor & Cochran 1989; Kozak et al. 2007):(3) where rx_a y is the simple correlation coefficient between an independent variable x_a and a dependent variable Y, Px_a y is the path coefficient between x_a and Y and presents the direct effect of x_a on Y, and r_an Px_n y is the simple correlation coefficient between x_a and x_n.

The statistical analysis was done using software STATISTICA Base, version 6.

Results and discussion

The grain yield of spring triticale differed between the experimental years (Table 1). The highest grain yield (5.11 mg ha⁻¹) was obtained in 2011, when the total amount of rainfall (262 mm) was close to the long-term mean (optimal humidity). This year, the daily average air temperature between the months of April through July was 1.6°C higher than the long-term average. Under these conditions, grain yield of spring triticale was high; however, the protein content decreased with increasing yield due to a dilution effect. The influence of the weather conditions on grain yield was confirmed by the studies on other cereals (Rharrabti et al. 2001; Gonzalez et al. 2010; Peymaninia et al. 2012). Fertilization significantly increased grain yield. In average, N fertilization resulted in 35.3% higher grain yield compared with the control treatment (N₀). Grain yield increased in line with N rate increasing up to N₁₂₀.

Table 1. Grain yield and protein content of spring triticale as affected by year and N fertilization.

	Grain yield (mg ha^−1)	Protein (%)
Year
2008	4.69 b^†	13.9 a^†
2009	4.54 c	13.5 a
2010	4.52 c	13.1 a
2011	5.11 a	11.4 a
Trial mean	4.72
N fertilization
N0	3.59 b^‡	11.4 b^‡
N60	4.53 a	12.0 a
N90	4.81 a	12.5 a
N120	4.92 a	13.0 a
N150	4.93 a	13.7 a
N180	5.09 a	13.9 a

Note: ^†Different letters in column denote a statistically significant difference (P ≤ .05) between years’ mean and trial mean.

^‡Different letters in column denote a statistically significant difference between fertilized treatments and unfertilized treatment (P ≤ .05).

In 2008, whose growing season may be described as moderately droughty, the values of all measured physiological indices were lower than those in the 2009–2011 growing seasons with excess humidity (Table 2). The lack of humidity resulted in lower N fertilizer efficiency and, consequently, in a decrease in SPAD and other physiological traits. The highest SPAD values were established in 2010 and 2011 in all growth stages. Maximum LAI was also attained in 2010 and 2011, but at BBCH 31 the highest LAI was measured in 2009. The effects of weather conditions and nitrogen fertilization on triticale physiological traits were documented by Gonzalez et al. (2010) and Kendal (2015).

Table 2. Physiological traits of spring triticale as affected by year and N fertilization.

Growth stage		SPAD^†	LAI	LAD	CGI
BBCH‡ 31	Year
	2008	44.6 b^§	1.06 c^§	–	44.6 c^§
	2009	41.5 c	2.26 a	–	96.7 a
	2010	50.2 a	1.75 b	–	74.1 b
	2011	41.6 c	1.69 b	–	73.1 b
	Trial mean	44.5	1.69	–	72.1
	N fertilization
	(1) N0	40.0 b^¶	1.13 b^¶	–	43.1 b^¶
	(2) N60	45.4 a	1.51 a	–	67.3 a
	(3) N90	44.7 a	1.77 a	–	76.9 a
	(4) N120	46.4 a	1.69 a	–	71.1 a
	(5) N150	45.7 a	1.81 a	–	76.6 a
	(6) N180	44.9 a	2.14 a	–	91.3 a
BBCH 45	Year
	2008	44.1 c^§	1.72 c	0.23 c	77.1 c
	2009	44.9 c	2.82 c	0.42 a	126.8 c
	2010	49.4 a	3.91 a	0.31 a	195.1 a
	2011	49.2 a	4.76 a	0.23 c	234.9 a
	Trial mean	46.9	3.30	0.30	158.5
	N fertilization
	(1) N0	41.8 b^¶	2.19 b	0.20 b	90.8 b
	(2) N60	46.4 a	3.28 a	0.29 a	150.3 a
	(3) N90	47.7 a	3.12 a	0.29 a	149.8 a
	(4) N120	49.2 a	3.45 a	0.31 a	175.7 a
	(5) N150	47.3 a	3.27 a	0.31 a	161.6 a
	(6) N180	48.1 a	3.90 a	0.37 a	193.3 a
BBCH 59	Year
	2008	52.2 b^§	2.30 c	0.13 c	120.8 c
	2009	51.5 b	2.65 c	0.34 c	136.8 c
	2010	51.4 b	4.39 a	0.38 c	220.2 a
	2011	53.2 a	2.98 b	0.97 a	160.6 b
	Trial mean	52.1	3.08	0.45	159.6
	N fertilization
	(1) N0	46.7 b^¶	1.97 b	0.28 b	95.8 b
	(2) N60	51.4 a	3.00 a	0.45 a	151.1 a
	(3) N90	52.3 a	3.46 a	0.47 a	178.2 a
	(4) N120	52.7 a	3.35 a	0.49 a	176.2 a
	(5) N150	54.7 a	3.18 a	0.45 a	169.4 a
	(6) N180	54.5 a	3.82 a	0.54 a	206.2 a
BBCH 69	Year
	2008	56.2 a^§	1.53 c	0.10 c	85.4 c
	2009	52.6 b	2.41 b	0.13 c	127.9 b
	2010	52.0 c	3.38 a	0.56 a	173.7 a
	2011	53.4 b	2.51 b	0.20 c	138.4 b
	Trial mean	53.6	2.46	0.24	131.4
	N fertilization
	(1) N0	47.7 b^¶	1.50 b	0.18 b	69.4 b
	(2) N60	51.3 a	2.36 a	0.25 a	122.5 a
	(3) N90	54.9 a	2.41 a	0.25 a	133.6 a
	(4) N120	54.5 a	2.76 a	0.28 a	144.9 a
	(5) N150	55.5 a	2.78 a	0.25 a	151.2 a
	(6) N180	55.3 a	2.88 a	0.28 a	158.3 a

Note: ^†SPAD – chlorophyll index, LAI – leaf area index, LAD – leaf area duration, CGI – canopy greenness index.

^‡BBCH-scale is a system for a uniform coding of phenologically similar growth stages of all mono- and dicotyledonous plant species.

^§Different letters in column denote a statistically significant difference (P ≤ .05) between years’ mean and trial mean.

^¶Different letters in column denote a statistically significant difference between fertilized treatments and unfertilized treatment (P ≤ .05).

N fertilization governed the physiological traits of triticale. At BBCH 31 and BBCH 45, the highest SPAD was in the N₁₂₀ treatment (Table 2). At BBCH 59 and BBCH 69, the highest SPAD was established under the highest N rates, N₁₅₀ and N₁₈₀; however, compared with N₁₂₀, the differences were insignificant. The highest rates (N₁₅₀ and N₁₈₀) resulted in the highest values of LAI, LAD and CGI.

The analysis of variance showed that the effect of N fertilization, year and their interaction on grain yield and protein was statistically significant (P ≤ .01) (Table 3). N fertilization was the main factor determining the grain yield differences between N treatments. The impact of N fertilization, year and their interaction on physiological indices was statistically significant (P ≤ .01) in most of the tested cases (Table 4).

Table 3. Mean squares (percentage of the sum of squares between parentheses) and significant effects of year and N rates on grain yield and grain protein in spring triticale over 4 years.

Source	df	Grain yield (mg ha^–1)	Grain protein (%)
Year (Y)	3	2.410** (13.4)	37.2** (44.1)
N fertilization (N)	5	3.720** (48.4)	15.8** (43.5)
Y × N	15	0.310** (12.2)	0.74** (6.1)

*Significant at the.05 probability level.

**Significant at the .01 probability level.

Table 4. Mean squares (percentage of the sum of squares between parenthesis) and effects of year, N rates and their interaction on physiological indices of spring triticale over 4 years.

Source	df	SPAD^†	LAI	LAD	CGI
BBCH 31^‡
Year (Y)	3	531.4** (49.6)	7.70** (43.7)	–	14564** (39.9)
N rate (N)	5	62.0** (13.5)	1.32** (17.5)	–	2984** (19.1)
Y × N	15	10.0 (6.5)ns§	0.41** (16.1)	–	817** (15.7)
BBCH 45^‡
Year (Y)	3	244.8** (31.7)	55.72** (58.1)	0.264** (52.8)	157987** (61.1)
N rate (N)	5	79.1** (23.9)	5.14** (12.5)	0.039** (18.1)	16205** (14.6)
Y × N	15	17.8** (16.1)	1.27** (9.3)	0.007** (9.4)	3330** (9.0)
BBCH 59^‡
Year (Y)	3	22.0* (3.4)	27.0** (33.7)	4.157** (84.3)	60800** (27.5)
N rate (N)	5	101.3** (38.3)	5.0** (14.5)	0.099** (4.6)	17739** (18.7)
Y × N	15	19.3** (21.9)	1.4* (12.6)	0.033** (4.7)	4601** (14.5)
BBCH 69^‡
Year (Y)	3	110.1** (11.5)	18.3** (39.3)	1.459** (85.4)	42369** (31.4)
N rate (N)	5	120.8** (29.5)	3.0** (15.2)	0.021** (2.9)	12042** (20.8)
Y × N	15	20.9 (15.3)ns	1.0** (15.4)	0.011** (4.6)	3055** (15.9)

Note: ^†SPAD – chlorophyll index, LAI – leaf area index, LAD – leaf area duration, CGI – canopy greenness index.

^‡BBCH-scale is a system for a uniform coding of phenologically similar growth stages of all mono- and dicotyledonous plant species.

*Significant at the.05 probability level.

**Significant at the.01 probability level.

§ns – nonsignificant.

The strength of the relationship between the parameters differed at different GS of spring triticale (Table 5). Grain yield positively and significantly correlated with SPAD at all growth stages; however, the relationship between the parameters was stronger at later GS. The grain yield relation with LAI was stronger at the stage of intensive growth. LAD significantly correlated with grain yield only at BBCH 59. The positive relationship between yield and CGI was significant (P ≤ .01) in most of the tested cases. Significant and quite clear relationship among grain yield of wheat, LAI and SPAD was confirmed by Saleem et al. (2010), Lepiarczyk et al. (2005) and Yildırım et al. (2010). Nakano et al. (2010) ascertained that grain yield of wheat was significantly and positively correlated with the LAI, SPAD and CGI at anthesis. In the study with spring triticale, slightly diverse results were obtained – the correlation between SPAD and yield was strong and significant only at the stage of intensive growth (Janušauskaitė 2009). Poblaciones et al. (2009) found that the relationship between wheat yield and SPAD improved when the readings were obtained at anthesis as opposed to stem elongation. According to Kulig et al. (2010), there was no significant correlation between SPAD values and grain yield of spring wheat; however, the SPAD showed a strong link with the grain protein.

Table 5. The correlation between spring triticale grain yield, grain protein and physiological indices for 2008–2011.

	Growth stage	SPAD^†	LAI	LAD	CGI
Grain yield	BBCH^‡ 31	0.252*	0.361**	–	0.401**
	BBCH 45	0.488**	0.437**	0.149	0.491**
	BBCH 59	0.460**	0.123	0.513**	0.176
	BBCH 69	0.406**	0.265*	0.018	0.334**
Grain protein	BBCH 31	0.183	0.147	–	0.141
	BBCH 45	0.049	−0.308*	0.375**	−0.267*
	BBCH 59	0.248*	0.072	−0.502**	0.083
	BBCH 69	0.272*	0.018	−0.055	0.057

*Significant at the.05 probability level.

**Significant at the.01 probability level.

^†SPAD – chlorophyll index, LAI – leaf area index, LAD – leaf area duration, CGI – canopy greenness index.

In the present study, protein content did not correlate with SPAD at the two first measurements, whereas the positive and statistically significant (P ≤ .05) correlation was detected at BBCH 59 and BBCH 69 (Table 5). The relationship of protein with LAI, LAD and CGI was significant only in several of the tested cases. These results agree with the finding of Poblaciones et al. (2009), who established strong positive relationship of protein with SPAD at BBCH 31 and BBCH 61. Similar data have been obtained in the study with other crops (Janušauskaitė 2009; Poblaciones et al. 2009; Nakano et al. 2010; Kendal 2015). A significant relationship was found in durum wheat between protein and SPAD at anthesis (Rharrabti et al. 2001; Yildırım et al. 2010).

Path analysis showed direct and indirect effect of causal variables on effect variables. In this method, the correlation coefficient between two traits is separated into the components which measure the direct and indirect effects (Peymaninia et al. 2012). The results of path analysis revealed that positive direct and dominant effect on grain yield was shown by CGI (Pxy = 0.984) at BBCH 31 (Table 6). This means that namely CGI as a dominant factor determined yield (Y) and had a proportion of 60.6% in positive correlation (r(Y) = 0.401**). LAI had negative direct effect on grain yield (Pxy = −0.625). Positive dominant indirect effect of CGI (rx₂×₄ Px₂ y = 0.963) mitigated negative action of LAI. SPAD had negligible positive direct effect on yield; however, dominant indirect effect of CGI determined strength and direction of the correlation.

Table 6. The path coefficients analysis showing direct and indirect effects of the physiological indices on grain yield of spring triticale.

		Effects via								Grain
		SPAD (1)		LAI (2)		LAD (3)		CGI (4)		yield
BBCH^†	Indices^‡	PC§	%^¶	PC	%	PC	%	PC	%	r Y
31	SPAD (1)	0.082^#	14.0	−0.167	28.4	–	–	0.338^††	57.6	0.252*
	LAI (2)	0.022	1.4	−0.625	38.8	–	–	0.963	59.8	0.361**
	CGI (4)	0.028	1.7	−0.611	37.6	–	–	0 . 984	60.6	0.401**
45	SPAD (1)	0.210	23.0	−0.213	23.4	0.004	0.4	0.485	53.2	0.487**
	LAI (2)	0.101	7.6	−0.445	33.5	0.022	1.7	0.759	57.2	0.437**
	LAD (3)	0.012	2.9	−0.136	32.4	0.072	17.1	0.200	47.6	0.149
	CGI (4)	0.132	9.7	−0.435	32.0	0.019	1.4	0 . 775	56.9	0.491**
59	SPAD (1)	0.483	37.6	0.254	19.7	0.137	10.6	−0.414	32.1	0.460**
	LAI (2)	0.162	18.2	0.759	38.4	0.130	6.6	−0.928	46.9	0.123
	LAD (3)	0.144	12.8	0.216	19.3	0 . 457	40.7	−0.305	27.2	0.513**
	CGI (4)	0.213	10.4	0.751	36.6	0.149	7.3	− 0 . 937	45.7	0.176
69	SPAD (1)	0 . 283	69.5	0.006	1.5	0.037	9.1	0.081	19.9	0.406**
	LAI (2)	−0.008	0.8	−0.216	21.1	−0.155	15.2	0.644	63.0	0.265*
	LAD (3)	−0.048	5.6	−0.156	18.2	−0.215	25.1	0.437	51.1	0.018
	CGI (4)	0.035	3.3	−0.212	20.3	−0.143	13.7	0 . 655	62.7	0.334**

Note: ^†BBCH-scale is a system for a uniform coding of phenologically similar growth stages of all mono- and dicotyledonous plant species.

^‡SPAD – chlorophyll index, LAI – leaf area index, LAD – leaf area duration, CGI – canopy greenness index.

^§PC – Path coefficient.

^¶% – percentage of direct and indirect effects.

^#Values in italics mean direct effects.

^††Underlined values mean dominant effects.

*Significant at the .05 probability level.

**Significant at the .01 probability level.

At BBCH 45, direct effects of physiological factors on grain yield ranged from Pxy = 0.072 in LAD to Pxy = 0.775 in CGI (Table 6). The highest direct, dominant and positive effect on yield was expressed by CGI (Pxy = 0.775). CGI acting as the dominant factor, governed direction and strength of grain yield (Y) correlation with SPAD (rx₁×₄ Px₁ y = 0.485, r(Y) = 0.487**) and LAI (rx₂×₄ Px₂ y = 0.759, r(Y) = 0.437**).

At BBCH 59, the highest direct and positive effects on yield were shown by LAI (to Pxy = 0.759) and SPAD (Pxy = 0.483) (Table 6). CGI exhibited high negative indirect effect over SPAD (rx₁×₄ Px₁ y = –0.414) and weakened the correlation between yield and SPAD (r(Y) = 0.460**). A large negative indirect and dominant effect of CGI on yield over LAI (rx₂×₄ Px₂ y = –0.928) was counterbalanced by the positive direct effect of LAI (Pxy = 0.759); it resulted in the weak insignificant correlation between LAI and yield (r(Y) = 0.123). The highest negative direct and dominant effect on yield was exerted by CGI; however, indirect positive effect of LAI (rx₄×₂ Px₄ y = 0.751) mitigated the adverse action of CGI.

At BBCH 69, CGI (Pxy = 0.655) had the highest positive direct and dominant effect on grain yield followed by SPAD (Pxy = 0.283) (Table 6). LAI and LAD had direct negative effect on yield, but GCI exhibiting high indirect positive and dominant effect over LAI (rx₂×₄ Px₂ y = 0.644) and over LAD (rx₃×₄ Px₃ y = 0.437) reduced the negative impact of them.

At BBCH 31, the largest positive direct effect on protein was recorded for LAI (Pxy = 0.507) (Table 7). SPAD had less positive direct effect (Pxy = 0.191). A fairly large negative indirect effect of CGI over SPAD (rx₁×₄ Px₁ y = −0.144) and over LAI (rx₂×₄ Px₂ y = −0.411) determined the correlation strength of protein with SPAD and LAI. CGI had negative direct effect on protein (Pxy = −0.420). LAI, acting as a dominant indirect positive factor, mitigated the negative action of CGI on protein. At BBCH 45, direct contribution of physiological indices on protein ranged from 0.693 in CGI to −1.246 in LAI (Table 7). Dominant negative direct and dominant indirect negative effects of CGI via LAI (rx₄×₂ Px₄ y = −1.219) determined the strength and direction of protein correlation with LAI and CGI. LAD acting as dominant direct effect (Pxy = 0.566), buffered indirect negative influence of LAI on protein. At BBCH 59, the highest direct dominant positive effect on protein was shown by LAI (Pxy = 0.798) and SPAD (Pxy = 0.488). CGI, acting as indirect negative factor, diminished the strength of protein correlation with SPAD (rx₁×₄ Px₁ y = −0.315; r(Y) = 0.248*) and LAI (rx₂×₄ Px₂ y = −0.706; r(Y) = 0.072). LAD had direct negative and dominant effect (Pxy = –0.643) on protein. The sum of indirect positive effects of SPAD (rx₃×₁ Px₃ y = 0.146) and LAI (rx₃×₂ Px₃ y = 0.227) mitigated the negative influence of LAD on protein content. CGI exhibited negative direct effect (Pxy = −0.714) on protein, but it had indirect positive effect through LAI and SPAD. LAI was the main cause of weak and positive correlation between protein and CGI. At BBCH 69, SPAD and LAI had direct dominant positive effect on protein. LAD and CGI had dominant indirect positive effect through LAI on protein.

Table 7. The Path coefficient analysis showing direct and indirect effects of the physiological indices on grain protein of spring triticale.

		Effects via
		SPAD (1)		LAI (2)		LAD (3)		CGI (4)		Protein
BBCH^†	Indices^‡	PC§	%^¶	PC	%	PC	%	PC	%	r Y
31	SPAD (1)	0.191 #	40.6	0.136	28.9	–	–	−0.144	30.5	0.183
	LAI (2)	0.051	5.3	0 . 507	52.3	–	–	−0.411	42.4	0.147
	CGI (4)	0.066	6.7	0.496††	50.5	–	–	−0.420	42.8	0.141
45	SPAD (1)	0.179	14.4	−0.597	48.0	0.033	2.7	0.434	34.9	0.049
	LAI (2)	0.086	3.9	− 1 . 246	57.1	0.173	7.9	0.678	31.1	−0.308*
	LAD (3)	0.010	8.8	−0.382	33.6	0 . 566	49.8	0.179	15.7	0.375**
	CGI (4)	0.112	5.2	−1.219	56.2	0.146	6.7	0.693	31.9	−0.267*
59	SPAD (1)	0 . 488	38.7	0.267	21.2	−0.192	15.1	−0.315	25.0	0.248*
	LAI (2)	0.164	8.9	0 . 798	43.1	−0.183	9.9	−0.706	38.1	0.072
	LAD (3)	0.146	11.7	0.227	18.2	− 0 . 643	51.5	−0.232	18.6	−0.502**
	CGI (4)	0.216	11.2	0.790	41.0	−0.209	10.8	−0.714	37.0	0.083
69	SPAD (1)	0 . 305	84.0	−0.010	2.8	0.012	3.3	−0.036	9.9	0.272*
	LAI (2)	−0.008	1.1	0 . 361	51.2	−0.051	7.2	−0.284	40.3	0.018
	LAD (3)	−0.052	9.0	0.260	45.1	−0.071	12.3	−0.193	33.5	−0.055
	CGI (4)	0.038	5.2	0.355	48.7	−0.047	6.4	−0.289	39.6	0.057

Note: ^†BBCH-scale is a system for a uniform coding of phenologically similar growth stages of all mono- and dicotyledonous plant species.

^‡SPAD – chlorophyll index, LAI – leaf area index, LAD – leaf area duration, CGI – canopy greenness index.

^§PC – path coefficient.

^¶% – percentage of direct and indirect effects.

^#Values in italics mean direct effects.

^††Underlined values mean dominant effects.

*Significant at the .05 probability level.

**Significant at the .01 probability level.

The multiple linear regression model (y = a + bx₁ + cx₂ + dx₃ + ex₄, where y – grain yield or protein, x₁ – SPAD, x₂ – LAI, x₃ – LAD, x₄ – CGI) showed that grain yield and protein were significantly influenced by the physiological indices. The interaction of all physiological indices influenced the grain yield from 27.2% to 38.6%. The physiological indices determined protein content by 42–44%.

Many scientists have reported that SPAD measurements can be used as a single and rapid tool to detect and select stable and high yield of plants (Rharrabti et al. 2001; Nakano et al. 2010; Yildırım et al. 2010; Wang et al. 2014; Kendal 2015). Poblaciones et al. (2009) had the opposite opinion and proposed that SPAD was not useful in predicting grain yield. Our correlation and path analyses suggest that the grain yield and protein were significantly influenced by all the physiological indices measured but the influence of SPAD and LAI was stronger and more significant than that of CGI and LAD.

In conclusion, N fertilization significantly (P ≤ .01) influenced the grain yield, protein content and all physiological indices. The grain yield and physiological indices showed that N₉₀ level was the best compromise for the yield and physiological indices.

Path analysis gave a more comprehensive picture than simple correlations. Thus, path analysis revealed that SPAD had positive direct dominant effect on yield only at BBCH 59 and BBCH 69 (50% of tested cases). LAI resulted in 19.3–38.8% of the correlation between yield and physiological indices. SPAD had positive direct dominant effect on protein at three principal GS and caused 38.7–84.0% of correlation. However, LAI showed positive direct dominant effect on protein in most of the measurements and indirect dominant effect 55% of all tested cases and determined 41.0–57.1% of the total protein correlation with physiological indices.

The determination of physiological indices can be used for spring triticale growth modelling and agronomic management for improved productivity and grain quality. SPAD and LAI values, established at BBCH 45–69, can be used for grain yield prediction and those estimated at BBCH 69 can be used for grain protein prediction.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Daiva Janusauskaite is a doctor of science, senior research worker in the department of Plant Nutrition and Agroecology, Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (Lithuania). Her research area is plant nutrition and diagnostic, with a focus on fertilizers use efficiency. Presently, she is involved in research on the physiological performance of the plants depending on soil tillage and fertilization systems.

Dalia Feiziene is a doctor of science, a senior research worker in the Department of Plant Nutrition and Agroecology, Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (Lithuania). She deals with soil chemical and physical quality in contrasting soil and crop management systems, soil CO₂ flux investigation. Presently, she is involved in soil greenhouse gas emission, plant nutrition investigations in a long-term soil management experiments.

Virginijus Feiza is the head researcher, a doctor of science and the head of the department of Soil and Crop Management, Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (Lithuania). His research interest is soil and crop management systems, soil physical properties investigation. Presently, he is involved in soil hydro-physical properties investigations in a long-term soil management experiments

ORCID

Daiva Janusauskaite http://orcid.org/0000-0003-2839-3566