1. Introduction
Water deficit is the major environmental factor that reduces growth, quality and productivity in the world’s most important cultivated crops [
1]. Depending on its intensity, frequency, and combination with different stress factors, the grain yield (GY) can be reduced by more than 50%, and this problem is expected to increase with the projected global climate change [
2,
3].
In Mediterranean-climate areas, wheat is often exposed to a progressive water deficit from flowering to the grain filling stage, leading to terminal drought stress [
4,
5]. Therefore, understanding the mechanisms underlying plant tolerance to drought scenarios is essential to enhance crop resilience, considering the impact of terminal drought on crop productivity in rain-fed areas of Mediterranean environments.
Senescence is the last developmental stage of plant cells, tissues, and organs and, in the case of monocarpic species such as wheat, leaf senescence occurs along with the whole plant. In annual crops, leaf senescence is under the control of a highly regulated genetic program, which ensures the remobilization and efficient translocation of assimilates such as carbohydrates, amino acids, and mineral nutrients accumulated in vegetative tissues towards developing grains [
6,
7]. Therefore, the availability of these assimilates in the grain is strongly influenced by the senescence timing. Consequently, the onset and rate of senescence influence key agronomical traits, including grain yield and quality, as well as nutrient content [
8,
9,
10].
Leaf senescence usually starts from the tip or margins of a leaf towards its base with the progressive dismantling of mesophyll cells in a coordinated manner to remobilize nutrients and ensure reproductive success. Chloroplasts are the first organelles to be dismantled, allowing a major portion of leaf lipid and proteins to be recycled, while the mitochondria and nucleus remain intact until the final stages of senescence [
6,
11], often associated with the visual loss of chlorophyll, which corresponds to the first external symptoms of leaf senescence. Nonetheless, the senescence process also involves a series of structural, metabolic and transcriptional changes [
6,
12].
In wheat, delayed senescence or stay-green phenotypes often correlate with yield due to extended periods of photosynthesis and the maintenance of the green canopy [
13,
14], although this is not always the case [
8,
15]. Specifically, stay-green refers to a heritable delayed foliar senescence character, which may improve the grain filling process even under stress conditions [
14]. Indeed, upon exposure to adverse environmental conditions, such as biotic and abiotic stress, the plant can initiate the process of senescence as an adaptive response to promote survival and reproduction [
11,
16]. In this context, drought prematurely induces the process of senescence, which leads to a loss in chlorophyll content, a decrease in canopy size and activity of photosynthesis, and reduced grain yield [
17]. Therefore, the relationship between senescence (i.e., senescence or stay-green traits) and plant productivity is complex, and its positive effect depends on the plant species, genotype, productivity parameter measured (i.e., biomass, grain yield), and environment [
8,
14,
18].
Different methods have previously been used to quantify the dynamics of senescence in the field at leaf and whole-plant levels. For instance, rapid evaluation by visual observation has made it possible to describe the fractional loss in the green leaf area of wheat [
19,
20], but the rate of chlorophyll loss taken for an individual leaf with the Minolta SPAD meter has also been reported to be useful [
13,
21]. Meanwhile, high-throughput phenotyping based on the normalized difference vegetative index (NDVI) has been used successfully to quantify the dynamics of senescence (i.e., stay-green traits), and might be useful in plant breeding [
22,
23,
24,
25]. As far we are aware, direct comparisons between leaf and canopy approaches to assess the stay-green dynamic have not been performed yet. Indeed, most of the studies that have performed comparisons between leaf and canopy indexes focus on estimating the crop N status for improving N management through dose optimization [
26,
27]. For other crops, it has been established that these properties also affect the expected yield [
28].
This study aimed to assess the association between the stay-green and agronomical traits of fourteen spring bread wheat (
Triticum aestivum L.) genotypes with contrasting tolerance to water stress. The set of genotypes was selected from a collection of 384 cultivars and advanced semi-dwarf lines of spring bread wheat characterized for different physiological and agronomical traits in field conditions [
5], and evaluated in field and greenhouse trials, under water-limited (WL) and well-watered (WW) regimes. The stay-green dynamic was evaluated at leaf and whole-plant levels based on leaf chlorophyll content (measured with a portable chlorophyll meter) and NDVI measurements, respectively. The adjustment of the chlorophyll content and NDVI data made it possible to obtain information about the senescence dynamic (stay-green traits) between genotypes and is useful as an indicator of genotypic performance under conditions with different levels of water stress and can be used in the identification and selection of superior wheat genotypes through the breeding process.
4. Materials and Methods
4.1. Plant Material and Growing Conditions
Two experiments were conducted in glasshouse and field conditions with a set of fourteen contrasting spring bread wheat (
Triticum aestivum L.) genotypes, listed in
Table 6. The genotypes were selected according to the yield tolerance index from a previous study [
5,
39]. Cultivars PANTERA-INIA and PANDORA-INIA feature a similar genetic background, but PANTERA-INIA is a Clearfield
® cultivar with resistance to the herbicide imidazolinone following the introduction of the Ser-Asn627 mutation into two acetolactate synthase (ALS) genes (
imi1 and
imi2), located in wheat on chromosomes 6B and 6D, respectively, into cv. PANDORA-INIA [
40].
4.2. Glasshouse Experiment
In 2015, a glasshouse experiment was conducted at the Plant Breeding and Phenomic Center, Universidad de Talca, Talca, Chile (35°24′19″ S, 71°37′59″ W). The glasshouse featured natural lighting and a heating system; the average temperature of the growing period was 20 °C and the relative humidity 48% (
Supplementary Material Figure S1). On 3 July 2015, ten seeds of each genotype were sown in 7.5 L circular pots filled with a 1:1:1 substrate mixture of organic soil (Anasac, Santiago, Chile), perlite, and river sand, representing a total dry weight per pot of 4.9 kg. After the emergence of the second leaf, the seedlings were thinned to five per pot. Two irrigation treatments were established from fully expanded flag leaf (Zadoks stage Z41) [
41]: 30% (water-limited) and 75% (well-watered) of field capacity of the substrate mixture. Before the establishment of treatments, pots were weighed and watered to 75% of field capacity and fertilized with Hoagland nutritive solution (Caisson Lab, Smithfield, UT, USA). Soil water content was monitored by an automatic EC-5 sensor (Decagon Devices Inc., Pullman, WA, USA) connected to an EM-50 data logger (Decagon Devices Inc., Pullman, WA, USA;
Supplementary Material Figure S2). The experiment was conducted in a randomized block design with four replications.
4.3. Field Experiment
The field experiment was set up during the 2017 season in Santa Rosa (36°32′ S, 71°55′ W), in the Mediterranean region of Chile. The genotypes were grown under well-watered (WW) and water-limited (WL) conditions. The cumulative rainfall during the experiment was 486 mm. Daily weather records (
Supplementary Material Figure S3) were obtained from a nearby station of the Instituto de Investigaciones Agropecuarias (INIA), Chillán, Chile.
The experimental design was a randomized complete block design with four replicates. Plots consisted of five rows of 2 m in length and 0.2 m distance between rows. The sowing rate was 20 g m−2 and the sowing date was 18 July. Plots were fertilized with 260 kg ha−1 of ammonium phosphate (46% P2O5 and 18% N), 90 kg ha−1 of potassium chloride (60% K2O), 200 kg ha−1 of sulpomag (22% K2O, 18% MgO and 22% S), 10 kg ha−1 of boronatrocalcite (11% B) and 3 kg ha−1 of zinc sulfate (35% Zn). Fertilizers were incorporated with a cultivator before sowing. During tilling, an extra 153 kg ha−1 of N was applied. Weeds were controlled with the application of Flufenacet + Flurtamone + Diflufenican (96 g a.i.) as pre-emergence controls and a further application of MCPA (525 g a.i.) + Metsulfuron-methyl (5 g a.i.) as post-emergents. Cultivars were disease-tolerant and no fungicide was used. Furrow irrigation was used for the WW condition: three irrigations of about 50 mm at flag leaf stage (Z37), heading (Z50), and middle grain filling (Z70). For the WL regime (rainfed condition), plots received the natural precipitation until heading, and after that, a plastic shelter was used to prevent rainfall during grain filling.
4.4. Anthesis Time and Thermal Time
In both experiments, the anthesis date was recorded through periodic observations, when approximately half of the spikes had already extruded anthers from the middle spikelets. Calendar dates of anthesis were then converted into accumulated thermal time (degree days, °Cd). Daily thermal time was calculated as the average of the maximum and minimum air temperature considering a base temperature of 0 °C.
4.5. Leaf Senescence
Leaf senescence was assessed by the non-destructive measurement of leaf chlorophyll content index (Chl) using a portable chlorophyll meter (Dualex Scientific, Force A, Orzay, France). In the glasshouse experiment, five leaves per pot were measured from flag leaf emergence onwards. In the field experiment, measurements were taken on six leaves, three points along each leaf, starting in the initial stages of grain filling. All measurements were taken on healthy, clean flag leaves in both experiments.
Data of Chl was fitted over the accumulated thermal time after anthesis (x) using a logistic function, with a model similar to that described by Xie et al. (2014) [
13]:
where
t50 is the thermal time to 50% senescence, the
rate is the rate of leaf senescence and
Chlmax corresponds to the maximum chlorophyll accumulated.
The curve fitting was used to estimate the following stay-green traits for each pot or plot: (i) the chlorophyll persistence phase, defined as the period between anthesis and the time when reaching 10% senescence (
tonset); (ii) the decay phase (
decay) from
tonset to
ttotal; (iii) the total duration of flag leaf senescence (
ttotal), i.e., the period from anthesis to 90% loss of
Chlmax; and (iv) the area under Chl curve (
AreaChl) was calculated from anthesis to 1000 °Cd, as a measure of the total flag leaf greenness of a genotype (
Figure 6). Curve fittings were performed with SigmaPlot 10.0 and the area under the Chl curve was calculated with the AREA.XFM transform.
4.6. Canopy Senescence
Canopy senescence was assessed by measuring the Normalized Difference Vegetation Index (NDVI) of each plot with a hand-held crop sensor (GreenSeeker, Trimble, CA, USA). The distance between the GreenSeeker and the plot was kept constant at around 60 cm, measuring only the central row to avoid pointing border rows. A total of eight measurements were taken during grain filling, starting in the initial stages and ending when all plots attained physiological maturity.
NDVI data were then fitted over the accumulated thermal time (x) after anthesis using a sigmoid function with a model similar to that described by Christopher et al. (2014) [
37]:
where
SR is the rate of canopy senescence,
NDVIdif corresponds to the difference in NDVI between the maximum (
NDVImax) and minimum (
NDVImin) value, and
X50 is the thermal time to 50% senescence. From the fit of NDVI versus thermal time (
Figure 7), the following stay-green trait was estimated for each plot: the area under NDVI curve (
AreaNDVI), calculated between anthesis to 1000 °Cd, as a measure of the canopy greenness of a genotype. Curve fittings were performed with SigmaPlot 10.0, and the area under the NDVI curve was calculated with AREA.XFM transform.
4.7. Agronomical Traits
In the glasshouse experiment, plants were harvested at maturity and dried in a fan-forced oven at 60 °C for 48 h. The spikes were counted and threshed manually. The following traits were evaluated at maturity: grain yield (GY), number of kernels per spike (KS), thousand-kernel weight (TKW), and harvest index (HI). In the field experiment, 1 m of an inside row was harvested at maturity and dried in the oven to determine HI. KS and TKW were determined from 25 spikes taken at random from the inside row. GY was assessed by harvesting the whole plot.
4.8. Statistical Analysis
Analysis of variance (ANOVA) for each phenotypic trait was applied to assess the effect of genotype (G), water regime (W: WL and WW), and their interaction. Pearson correlations between different traits were computed using the average values across replicates. Phenotypic data were transformed to improve the normality of trait distribution when necessary. All the statistics and graphics were performed using the Statistical Software R version 3.3.3.