Effects of Planting Date and Genotypes on Potato Growth and Yield Determination in a Sub-Tropical Continental Growing Environment

doi:10.21203/rs.3.rs-4720912/v1

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Research Article

Effects of Planting Date and Genotypes on Potato Growth and Yield Determination in a Sub-Tropical Continental Growing Environment

https://doi.org/10.21203/rs.3.rs-4720912/v1

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Potato tuber yield-determining seasonal changes, especially in subtropical growing settings, are poorly investigated. This study examined eleven potato genotypes ‘G’ under four planting dates ‘P’ (very early (02 Oct), early (14 Oct), late (26 Oct), and very late (07 Nov)) and their interactive response (P×G) on potato growth and tuber yield in southern Khyber Pakhtunkhwa, Pakistan over two years (2017-18 and 2018-19). Early planting improved most yield-determining traits over late planting, extending the growing period (PM_D) and maximizing green canopy cover (C_G, 72.3%), mother stems plant^-1 (M_SN, 4.1), leaf number plant^-1 (L_N, 50.8), leaf area plant^-1 (LA_P, 5343 cm²), cumulative photosynthetic active radiation (PAR_INTC, 900.9 MJ m^-2), thermal days (TD_C, 52.9 td), and tubers plant^-1 (T_N, 11.8), marketable tuber weight (T_WM, 103.0 g), marketable (T_YM, 30.7 t ha^-1), and total (T_YT, 32.9 t ha^-1) tuber yield. Late plantings reduced PM_D and thus these traits. Considerable genotypic variation was found in plant phenology, growth, and tuber yield traits, however, genotype ranking also varied by planting date, indicating strong P×G interactions. The genotype ‘Arizona’ outperformed others with maximum T_YM (29.2 t ha^-1) and T_YT (30.4 t ha^-1) across planting dates. We identified key traits including days to emergence (E_D), C_G, PAR_INTC, and TD_C, which are vital indicators of yield potential and important for breeding and selection. Our findings highlight the complexity of yield formation in potatoes and suggest tailored genotype selection and planting strategies to enhance yield stability and resilience, which are crucial for adapting to climate change and meeting food demand.

Adaptation

crop management

planting time

plant phenology

seasonal variation

yield

Potato (Solanum tuberosum L.) is a major food crop worldwide (Calıskan et al., 2022), ranking fourth in terms of production after maize, rice, and wheat (FAOSTAT, 2021). It is a significant source of carbohydrates, proteins, vitamins, and minerals for millions of people, particularly in developing countries where it is a staple food (Lizana et al., 2017). Furthermore, potato is regarded as the leading substantial food crop owing to its short growing cycle and maximum energy and protein per unit area, even higher than wheat and rice (Van der Zaag, 1984; Hamm and Monique, 1999; Thiele et al., 2010; Devaux et al., 2014). Potato will be essential in tackling major food insecurity concerns and boosting smallholder farmers' incomes in the context of developing countries faced with burgeoning populations (Scott et al., 2019; Devaux et al., 2021).

The global demand for potatoes is increasing due to its agronomical plasticity and ability to thrive in diverse agro-climatic conditions (Haverkort, 1990; Haverkort and Struik, 2015). However, diverse factors including biotic and abiotic stresses, growing environments, soil properties, crop management, and genetic makeup largely contribute to yield gaps despite the widespread cultivation of potatoes (Van Der Zaag and Doornbos, 1987; Oliveira et al., 2016; Raymundo et al., 2017; Eid et al., 2020). Closing the potato production gap is therefore crucial for preserving food supplies for the foreseeable future (Calıskan et al., 2022).

Potato tuber yield production is the result of multiple plant functions that are influenced by environmental factors and genotype (Kooman et al., 1996a,b). The seasonal variations in a particular area may directly affect plant phenology thereby affecting the potential crop productivity (Samberg et al., 2010). The accumulation and rate of dry matter in tubers depend upon the capacity of genotype to intercept and utilize incident photosynthetic active radiation (Struik et al., 1990). The rate of photosynthesis and respiration, dry matter production, and its translocation to different parts of the plant, including tubers, are all affected directly or indirectly by temperature (Ewing, 1990; Squire, 1995). Furthermore, the interactive response of a developing plant to seasonal changes is very complex. For instance, a 5 ºC increase in optimum temperature (20 ºC) for photosynthesis may result in a 25% reduction in the photosynthesis rate (Burton, 1981) causing differential yield potential of any genotype across growing environments. This is the main reason that the yield production in potato is generally higher under temperate as compared to tropical growing conditions (Beukema and Van der Zaag, 1990). Furthermore, tuber yield in potato is a complex trait and considered polygenic in nature, displaying high variations (Bradshaw et al., 2006) and genotype-by-environment (G×E) interactions (Bombik et al., 2007; Rymuza and Bombik, 2010; Flis et al., 2014; Bradshaw, 2021).

Considering the huge diversity in growing environments, screening, and identification of suitable genotypes for particular and/or wider environments is very important. A successful potato genotype can produce good tuber yield that is stable over a wide range of growing conditions. However, screening of high producing and better adapted genotypes is challenging due to G×E interaction (Thiyagu et al., 2013). Therefore, the objective of potato breeding programs is the identification of unchanging and adapted genotypes for a wide range of growing environments (Lee et al., 2003; Koemel et al., 2004). A genotype is deemed to possess wide adaptation when its yield surpasses that of a reference genotype over a range of environments, or narrow adaptation when its yield outperforms the reference genotype in specific growing environments (Bustos-Korts et al., 2018).

In many potato-growing regions, farmers use traditional planting schedules that are based on local weather patterns and traditional knowledge. However, with changing climatic conditions and evolving farming practices, there is a need to re-evaluate these planting schedules and explore the potential benefits of alternative planting dates and/or variable environmental conditions. Additionally, there is a need to identify potato genotypes that are better adapted to specific environmental conditions and farming systems. Furthermore, there has been a lack of research on the variations in potato tuber yield-determining processes in the sub-tropical continental growing conditions of Pakistan. Hence, understanding the causes of variability in tuber production is essential for both physiologists and agronomists to make the best possible use of natural resources. This information is indispensable for making informed decisions regarding genotype selection and identifying appropriate agronomic practices.

Numerous studies have examined the effect of planting date on the growth and yield of wheat, maize, cotton, and other crops under changing climatic conditions. These studies have indicated that altering planting dates could be an effective adaptation strategy (Steyn et al., 1996; Seifert et al., 2006; Dornbusch et al., 2011; Paz et al., 2012; Li et al., 2022; Wu et al., 2023). The effect of planting time on potato growth and yield has also been studied extensively, but the results have been inconsistent and context specific (Hassanpanah et al., 2009; Arab et al., 2011; Wang et al., 2015, Darabi and Omidvari, 2020). Moreover, most of these studies have focused mainly on planting time with limited attention to genotype and their interaction effects. There is a lack of information regarding the ability of potato genotypes to adapt to different growing conditions, especially in the sub-tropical continental growing conditions of Pakistan. Additionally, there is limited understanding of the specific characteristics and mechanisms that drive adaptation in such production environments. Therefore, there is a need for more comprehensive and systematic research to elucidate the complex interplay between these factors and their combined effects on potato yield.

Therefore, the objective of this study was to analyze the performance of different potato genotypes under varying planting dates (or seasons) during two experimental years and to assess their interactive response on potato growth and tuber yield in the sub-tropical continental growing conditions of southern Khyber Pakhtunkhwa (KPK), Pakistan. Data were collected on several phenological, vegetative, and physiological characteristics, and their connections to determining tuber yield were evaluated. The results will guide breeding endeavours to address the complexities of genotype-by-seasonal interactions in sub-tropical agricultural settings. Additionally, they will contribute to the development of more effective management strategies and provide insights into the traits and factors that should be considered when developing varieties tailored to specific regions.

Site description and experimental field

Two years of field experiments were conducted to evaluate the performance of different genotypes under varying planting dates and assess the interaction effects between planting date and genotype on potato growth and tuber yield under sub-tropical conditions. The field experiments were conducted at the research site of the Department of Horticulture, Faculty of Agriculture, Gomal University, Dera Ismail Khan, KPK, Pakistan during the winter growing seasons (October-March) of experimental years 2017-18 (Y₁) and 2018-19 (Y₂). The experimental field is located between latitude 31º 43' 27.04'' N, longitude 70º 49' 42.94'' E and at elevation of 177 m above sea level. The region has diverse climatic conditions ranging from sub-arid to subtropical with 180 to 300 mm annual precipitation (Khan et al., 2010). Its total geographical area is 896,000 ha, with a mere 10% cultivated and 1% forest. About 50% of the geographical area is still barren and can be exploited for crop production. The area has two distinct seasons i.e., winter-season (locally called Rabi) starting from October‒March with a mean maximum air temperature of 24 ºC (± 5.5) and a summer-season (locally called Kharif) starting from April‒September with a mean maximum air temperature of 36 ºC (± 4.0). During the experimental period, required meteorological data (minimum, maximum, and mean air temperature, daylength, daily sunshine hours, and rainfall) were recorded at a nearby weather station of the Pakistan Meteorological Department (Fig. 1). Daily global solar radiation values were calculated from the daily sunshine hours during both the experimental years following the Ångström method (Ångström, 1924). The soil temperature at mid-day was recorded daily during the growing season using a digital soil thermometer (Rapitest, USA).

Field soil samples at a 0 ~ 30 cm depth were collected from three random spots of the experimental field to determine the soil physical and chemical characteristics two weeks’ prior to the start of the experiments (Table 1). The soil samples underwent a series of procedures, including air-drying, grinding, and passing through a 2 mm sieve, to evaluate the particle size distribution, as described by Koehler et al. (1984). The soil pH was determined using an electrometric method with a portable pH metre (JENWAY-3020). The measurement of electrical conductivity (EC) was conducted using a conductivity meter (inoLab). The determination of organic matter (OM) was conducted following the methodology established by Nelson and Sommers (1982). Total nitrogen was determined according to Bremner and Mulvaney (1982). The phosphorus and potassium contents were assessed using the methodology outlined by Sultanpour and Schwab (1977). The soil composition consisted of sandy loam with a slightly alkaline pH range of 8.0‒8.3. It was free from salt, with an electrical conductivity (EC) of 0.91‒0.95 dS m^− 1. The soil had a low organic matter content of 0.95‒0.84% and was considered to have sufficient levels of total N (0.08‒0.06%) and potassium (170.4‒168.9 mg kg^− 1), and was marginal in phosphorus (1.9‒1.8 mg kg^− 1) in Y₁ and Y₂, respectively. The soil series of the experimental site was Zindani, and the soil class was Typic Torrifluent (Soil Survey Staff, 2009).

Table 1

Physical and chemical characteristics of the topsoil layer (0–30 cm) for the two experimental years.
Parameter	Unit	Experimental year^†
		Y₁	Y₂
AB-DTPA extractable P	mg kg^− 1	1.9	1.8
AB-DTPA extractable K	mg kg^− 1	170.4	168.9
Total N	%	0.08	0.06
Organic matter	%	0.95	0.84
pH	₋	8.0	8.3
EC	dS m^− 1	0.91	0.95
Clay	%	1.6	1.7
Silt	%	44.0	45.3
Sand	%	54.3	53.0
Textural class	₋	Sandy loam	Sandy loam
^†Y₁ = 2017-18; Y₂ = 2018-19

Plant material

Eleven contrasting potato genotypes i.e., Elodie, El Beïda, Red Sun, Red Valentine, Rock, Arsenal, Constance, Arizona, Sarpo Mira, Désirée, and Fado, were selected for this research. These genotypes were chosen for their pronounced variability in plant traits. The genotype Désirée was included as a check due to its prevalence among local potato farmers. The seed tubers of these afore-mentioned genotypes were acquired from the potato program of National Agricultural Research Centre, Islamabad, Pakistan. The origin and key characteristics of the planting material is given in Table 2.

Table 2

Origin and background history of planting material.
Serial No.	Genotype	Acronym	Origin	Breeding company	Skin colour
1	Elodie	EL	France	INRA	White to Yellow
2	El Beïda	EB	France	Triskalia	Pale Yellow
3	Red Sun	RS	The Netherlands	De Nijs	Red
4	Red Valentine	RV	The Netherlands	De Nijs	Red
5	Rock	RK	The Netherlands	Meijer	Yellow
6	Arsenal	AS	The Netherlands	Agrico	Yellow
7	Constance	CS	The Netherlands	Agrico	Yellow
8	Arizona	AZ	The Netherlands	Agrico	Yellow
9	Désirée	DS	The Netherlands	HZPC	Red
10	Sarpo Mira	SM	The United Kingdom	Sarpo	Pink
11	Fado	FA	Scotland	The James Hutton Institute	Red

Experimental design and field management

The field trials were conducted using a split-plot randomized complete-block design (RCBD) with two factors, namely planting date and genotype, and three replications. The main plot comprised of four different planting dates: very-early: 02 Oct (T₁), early: 14 Oct (T₂), late: 26 Oct (T₃), and very-late: 07 Nov (T₄). The sub-plots consisted of eleven different potato genotypes (Table 3). Consequently, the genotypes were intentionally subjected to seasonal fluctuations resulting from the varying planting dates. The experiment thus consisted of 132 plots (04 planting dates×11 genotypes×03 replicates). Each plot (16.2 m²) had 6 ridges with 12 plants each. Soil preparation was performed with a disk plough, rotavator, and cultivator.

Table 3

List of traits and their methods of determination.
No.	Trait	Acronym	Unit	Method of determination
1	Days to 50% plant emergence	E_D	‒	The period (days) required for attainment of fifty percent plant emergence were counted after planting of clones.
2	Plant emergence	E_P	%	This was quantified using following formula as: \(\:{E}_{\text{P}}=\:\frac{\text{Number\:of\:plants\:emerged}}{\text{Sum\:of\:clones\:planted\:}}\:\times\:100\:\)
3	Green canopy cover	C_G	%	The green canopy cover was visually observed on bi-weekly basis by using a grid as described by Burstall and Harris (1983) and maximum value was estimated.
4	Plant height	H_P	cm	This was recorded at the end of crop cycle with measuring stick.
5	Number of mother stems plant^− 1	MS_N	‒	This was recorded at the end of crop cycle.
6	Number of leaves plant^− 1	L_N	‒	Aggregate of leaves appeared during the crop cycle were recorded.
7	Leaf area plant^− 1	LA_P	cm²	This was obtained by dividing the average leaf area (LA) of the plant by the total number of leaves on the plant. LA was quantified following Omolaiye et al. (2015) as: \(\:LA=0.41(L\times\:W\)) where L and W stand for the maximum lamina length and width, respectively.
8	Days to plant maturity	PM_D	‒	This was determined by counting the total period (days) from E_D to attainment of physiological maturity (i.e., 90% of the canopy senescence).
9	Cumulative PAR intercepted	PAR_INTC	MJ m^− 2	C_G was converted to PAR-interception percentage following Burstall and Harris (1983). Daily values of PAR_INT were summed to obtain PAR_INTC.
10	Cumulative thermal days	TD_C	td	The approach of Yin et al. (2005) and Khan et al. (2019) was used for conversion of the actual days into thermal days (TD). Daily values of TD were summed to obtain TD_C.
11	Number of tubers plant^− 1	T_N	‒	Total tubers formed were counted at plant maturity.
12	Mean weight of marketable tuber	T_WM	g	Mean tuber weight (≥ 35 mm) was found by using analytical balance.
13	Marketable tuber yield	T_YM	t ha^− 1	Tuber (≥ 35 mm) yield obtained from the plot area of 4.05 m² was calculated and transformed to t ha^− 1.
14	Unmarketable tuber yield	T_YU	t ha^− 1	Tuber (< 35 mm) yield obtained from the plot area of 4.05 m² was calculated and transformed to t ha^− 1.
15	Total tuber yield	T_YT	t ha^− 1	Tuber yield from the plot area of 4.05 m² was calculated and transformed to t ha^− 1.

Nitrogen (33.3% of total 150 kg ha^− 1 as urea), phosphorus (100 kg P₂O₅ ha^–1 as single super phosphate), and potassium (100 kg K₂O ha^–1 as sulphate of potash) were applied at planting time, while the remaining 66.6% of total N was applied in equal splits at 30 and 45 days after planting. Fertilizer was applied by the side banding method.

Healthy and uniform sized seed tubers of afore-mentioned potato genotypes were planted at a depth of 10 cm depth on the ridge, one seed per hole and 0.3 m apart. A gap of 0.75 m was maintained between any two ridges. Standard practices were followed during planting of the seed tubers. Local recommended cultural practices were followed to sustain normal crop growth without biotic and/or abiotic stress. Plots were irrigated on a weekly basis with each plot receiving a total of 600 (± 20) mm of irrigation during both the growing seasons.

Plant measurements

Observations on plant phenology, growth, and yield attributing parameters were recorded and compiled. Measurements on the plant phenology and vegetative growth determining traits including the number of days to 50% plant emergence, plant emergence (%), green canopy cover (%), plant height (cm), number of leaves plant^− 1, number of mother stems plant^− 1, leaf area plant^− 1 (cm²), and the number of days to plant maturity was recorded for the second and fifth rows of each plot. The yield determining traits including the number of tubers plant^− 1, mean weight of marketable tuber (g), total tuber yield (t ha^− 1), and marketable tuber yield (t ha^− 1) was recorded from the two central rows (i.e., third and fourth) of each plot. The first and sixth rows were excluded from the observations to avoid the border effect. The measurement of all traits followed the Distinctness, Uniformity, and Stability (DUS) standard standards set by the International Union for the Protection of New Varieties of Plants (UPOV) and IPGRI (1991). The traits and the specifics of their measurements are detailed in Table 3.

Calculation of climatic variables

The cumulative intercepted photosynthetically active radiation (PAR_INTC, MJ m^− 2) for the whole growth period was estimated following the approach of Khan (2012) and Khan et al. (2019b). The percentage green canopy cover (C_G) were transformed into percentage of PAR intercepted (PAR_INT), using the linear equation developed by Burstall and Harris (1983), which is PAR_INT (%) = 0.956 × canopy cover (%) – 4.95. The daily values of PAR_INT were added together to calculate the PAR_INTC. The daily incident PAR was determined as 50% of the total global solar radiation according to Spitters (1988). The time variables and duration were expressed in days; however, thermal days were also computed to assess the influence of daily and seasonal air temperature fluctuations under field conditions (McMaster and Wilhelm, 1997). The procedure of beta thermal time proposed by Yin et al. (1995; 2005) was adopted for the calculation of thermal days (td). This method is more resilient and takes into consideration the non-linear correlation between temperature (T) and the rate of growth or development (g(T)), which is determined by three cardinal temperatures: the base temperature (T_b), the optimal temperature (T_o), and the ceiling temperature (T_c). The temperature estimates of T_b = 5.5 ˚C, T_o = 23.4 ˚C, and T_c = 34.6 ˚C were employed based on the research conducted by Khan (2012) and Khan et al. (2019a,b).

Defining genotype maturity type

The genotypes were categorized into four maturity classes (very early (VE), early (E), late (L), and very late (VL)) based on the number of days it took for them to reach maturity (PM_D). This categorization was done using the Ward's minimum-variance clustering method in SAS software (SAS Institute Inc. 2004), following the strategy described by Khan et al. (2013). Figure 2 provides a schematic illustration of cluster analysis used to determine distinct maturity classes. The input data consisted of the pooled mean of eleven genotypes across two experimental years.

Statistical analyses

Based on a preliminary analysis of the data, it was found that the interactions between the experimental year (Y), planting date (P), and genotypes (G) were not statistically significant, as shown in Table 5. Therefore, the data from the two experimental years were combined and analyzed using a general analysis of variance, considering main effects of P, G, and their interactions (P×G). The means of each trait were compared using Fisher's protected least significant difference (LSD) test. The Pearson correlations were used to assess the interrelationships among the traits. A stepwise multiple linear regression analysis was used to investigate the correlation between tuber yield, the dependent variable, and the other traits, which were treated as independent or predictor variables. The objective was to ascertain the key traits that contribute to the highest variability in tuber yield. During the initial round, each individual trait (predictor variable) was sequentially included to the regression model depending on the increasing amount of variance it explained in tuber yield. This was done using a forward selection technique. The predictor variable with the highest regression coefficient (R²) was included in the model, while the others were excluded (James et al., 2014; Peter and Bruce, 2017). This strategy was implemented until the point where the value of R² could no longer be enhanced by including additional independent variables. In the second round, each independent variable that was picked in the first round was progressively removed from the regression model one by one. This was done based on the reduced amount of variance explained by the model, using a backward selection method. This approach was carried out until the value of R² could not be further decreased by the reduction of independent variables. All the statistical techniques were performed using the Genstat software package (Payne et al., 2009).

Table 5

Stepwise regression analysis of different traits using total tuber yield T_YT (t ha^− 1) as a dependent variable.
Trait^a		Selected model number (#)
Trait^a	#3	#5	#11	#12	#16	#19	#22	#23
E_D	+	+	+	+	+	+	+	+
E_P (%)	+	+	+	+	+	+	₋	₋
C_G (%)	+	+	+	+	+	+	+	+
H_P (cm)	₋	+	+	+	+	+	₋	₋
MS_N	₋	+	+	+	+	₋	₋	₋
L_N	₋		₋	₋	₋	₋	₋	₋
LA_P (cm²)	₋		+	+	+	₋	₋	₋
PM_D	₋		+	+	+	₋	₋	₋
PAR_INTC (MJ m^− 2)	₋		+	+	+	+	+	₋
TD_C (td)	₋		+	+	₋	+	+	+
T_N	₋		+	+	₋	₋	₋	₋
T_WM (g)	₋			+	₋	₋	₋	₋
R^{2 **}	65.2	69.0	95.3	96.3	95.5	95.0	94.7	94.2
^** Significant at P ≤ 0.001
^a The ‘+’ symbol denotes the independent trait whose mean value was included in the regression model, ‘-’ symbol denotes the independent trait whose mean value was exempted from the regression model.
Where E_D = Days to 50% plant emergence, E_P = Plant emergence, C_G = Green canopy cover, H_P = Plant height, MS_N = Number of mother stems plant^− 1, L_N = Number of leaves plant^− 1, LA_P = Leaf area plant^− 1, PM_D = Days to plant maturity, PAR_INTC = Cumulative PAR intercepted, TD_C = Cumulative thermal days, T_N = Number of tubers plant^− 1, T_WM = Mean weight of marketable tuber.

Assessment of weather conditions

The weather conditions created by different planting dates are presented in Fig. 1. The earlier planting date exhibited a broader range of values in most of the meteorological parameters (Fig. 1).

The mean minimum air temperature was comparatively higher in the earlier than in the later planting dates from 0–29 DAP, 30–59 DAP, and 60–89 DAP, and was higher in the later than in the earlier planting dates from 90 DAP until plant maturity.

The µean µiniµuµ air teµperature was highest (18.9°C) for very early planting date followed by early planting date (17.2°C) at the start of growing season (0–29 DAP), dropped sharply to 5.8°C and 6.0°C at 90–119 DAP, started rising again from 120 DAP until plant maturity (9.3°C and 11.6°C, respectively). In case of late and very late planting dates, the mean minimum air temperature started at 14.9°C and 12.7°C in the beginning of growing season (0–29 DAP), decreased to 6.2°C and 5.9°C at 60–89 DAP and then started rising again from 90 DAP (20.8°C) peaking at 12.7°C and 14.3°C, respectively at maturity.

The µean µaxiµuµ air teµperature was highest (28.8°C) for very early planting date at the start, decreased steadily to 16.8°C at 90–119 DAP, then rose to 20.8°C at plant maturity. In case of early and late planting dates, the mean maximum air temperature started at 25.5°C and 22.6°C decreased to 16.7°C at 60–89 DAP and then peaked at 22.7°C and 24.4°C, respectively at maturity. In case of very late planting date, mean maximum air temperature was 21.1°C in the

beginning dropped to 17.5°C at 60–89 DAP and then started rising at 90–119 DAP (20.8°C) peaking at 27.4°C at plant maturity.

The µean air teµperature was highest (24.0°C) for very early planting date at the start (0–29 DAP), declined to 11.3°C at 90–119 DAP, then rose to 15.1°C at plant maturity. In case of early planting date, the mean air temperature started at 21.4°C decreased to 11.5°C at 60–89 DAP and then peaked to 17.1°C at maturity. In case of late and very late planting dates, mean air temperature started lowest at 18.9°C and 17.0°C, dipped to 11.4°C and 11.7°C at 60–89 DAP and then peaked to 18.7°C and 20.8°C, respectively at maturity.

The µean soil teµperature was highest (22.0°C) for very early planting date at the start (0–29 DAP), declined to 10.9°C at 90–119 DAP, then rose to 14.3°C at plant maturity. In case of early planting date, the mean soil temperature started at 20.3°C and decreased to 10.2°C at 60–89 DAP and then peaked to 16.2°C, respectively at plant maturity. In case of late and very late planting dates, mean soil temperature started lowest at 17.0°C and 15.1°C, dipped to 10.0°C and 9.8°C at 60–89 DAP and then peaked to 17.0°C and 19.9°C at maturity, respectively.

The daylength was maximum (11:10 and 10.48 hours) in the beginning (0–29 DAP) and decreased sharply to 09:57 hours at 60–89 DAP and later extended from 90 DAP peaking at 10:56 and 11:12 hours at maturity in case of very early and early planting dates, respectively. For late and very late planting dates, daylength started shorter at 10:29 and 10:14 hours and started extending from 60 DAP peaking at 11:26 and 11:36 hours at maturity, respectively.

The solar radiation was higher in the earlier planting dates throughout the growing season in comparison to later planting dates. The early planting date had higher accumulated maximum solar radiation (712.6 MJ m^− 2) followed by very early planting date (562.2 MJ m^− 2) in the start of growing season (0–29 DAP). The solar radiation decreased gradually at 90–119 DAP (492.0 and 478.8 MJ m^− 2) and later peaked at maturity (596.7 and 497.3 MJ m^− 2) in both planting dates, respectively. The late and very late planting dates accumulated comparatively lower solar radiation (530.1 and 514.4 MJ m^− 2) in the beginning of growing season (0–29 DAP), later fluctuated and then dropped significantly to 353.8 and 138.6 MJ m^− 2, respectively at plant maturity. It was interesting to note that daylength increased at the time of plant maturity in very late planting, however, high maximum air temperature (27.4°C) had a detrimental effect on foliage resulting into early plant maturity and reduction in accumulated solar radiation (Fig. 1).

The rainfall varied among the planting dates and was especially high between 120 DAP to maturity for very early (40.5 mm) and early (34.0 mm) planting dates and from 90 to 119 DAP for late (23.5 mm) and very late (28.0 mm) planting dates.

Effect of planting date on phenology and vegetative growth

Planting date had a significant (P ≤ 0.01) impact on all the traits determining plant phenology and vegetative growth of potato (Table 4).

Table 4

Analysis of variance in F values of potato traits among two growing seasons, four planting dates, and eleven potato genotypes.
	E_D	E_P (%)	C_G (%)		H_P (cm)	MS_N
Experimental year (Y)	325.13**	4.17*	16942.63**		3258.95**	1178.58**
Planting date (P)	35407.07**	460.78**	10969.09**		8499.20**	981.77**
Genotype (G)	11838.95**	32.09**	2685.42**		1845.17**	929.06**
Y×G	˗	˗	˗		˗	˗
Y×P	˗	˗	˗		˗	˗
P×G	1565.77**	23.01**	152.11**		129.99**	157.71**
Y×P×G	˗	˗	˗		˗	˗
	L_N	LA_P (cm²)	PM_D		PAR_INTC (MJ m^− 2)	TD_C (td)
Experimental year (Y)	1715.64**	1167.69**	325.13**		2264.32**	35.55**
Planting date (P)	2311.14**	1450.12**	1.175E + 05**		53873.67**	11766.24**
Genotype (G)	528.82**	233.63**	11838.95**		8943.67**	1789.67**
Y×G	˗	˗	˗		˗	˗
Y×P	˗	˗	˗		˗	˗
P×G	36.98**	19.82**	1565.77**		1426.68**	303.80**
Y×P×G	˗	˗	˗		˗	˗
	T_N	T_WM (g)		T_YM (t ha^− 1)	T_YU (t ha^− 1)	T_YT (t ha^− 1)
Experimental year (Y)	260.20**	471.48**	10530.11**		255.66**	12834.64**
Planting date (P)	439.37**	351.53**	17705.45**		1941.98**	17136.20**
Genotype (G)	270.57**	96.36**	1017.73**		2269.63**	856.13**
Y×G	˗	˗	˗		˗	˗
Y×P	˗	˗	˗		˗	˗
P×G	9.92**	4.83**	59.37**		204.4**	39.80**
Y×P×G	˗	˗	˗		˗	˗
^** Significant at P ≤ 0.01; ^* Significant at P ≤ 0.05; ˗ Non-significant
Where E_D = Days to 50% plant emergence, E_P = Plant emergence, C_G = Green canopy cover, H_P = Plant height, MS_N = Number of mother stems plant^− 1, L_N = Number of leaves plant^− 1, LA_P = Leaf area plant^− 1, PM_D = Days to plant maturity, PAR_INTC = Cumulative PAR intercepted, TD_C = Cumulative thermal days, T_N = Number of tubers plant^− 1, T_WM = Mean weight of marketable tuber, T_YM = Marketable tuber yield, T_YU = Unmarketable tuber yield, T_YT = Total tuber yield.

Plants required 12.0 to 23.3 days for emergence (E_D) as a resultant of different planting dates (Fig. 3). E_D advanced with a delay in planting and vice versa. Minimum E_D (12.0 days) was recorded for very late followed by late planting (14.02 days). Both planting dates were statistically unique from each other. A maximum delay in plant emergence (23.3 and 15.9 days) was noted for the very early and early planting dates, respectively. However, both these planting dates were statistically different from each other.

E _P ranged between 73.8 and 99.2% across different planting dates (Fig. 3). E_P increased for the later planting dates. The highest and statistically similar E_P (99.2 and 98.4%) was observed for the very late and late planting dates, respectively. E_P was smallest (73.8%) for the very early planting succeeded by early planting (95.1%). However, both planting dates were statistically different.

PM _D varied across the planting dates and ranged from 115 to 134 (Fig. 3). Days required for plant maturity declined with a delay in planting and vice versa. Seed-tubers planted very late took the smallest number of days to maturity (115 days). It was succeeded by late planting taking 125 days to plant maturity. A maximum delay in plant maturity (134 days) was found for the very early planting followed by early planting (132 days). All these planting dates were statistically different from each other.

C _G ranged from 43.0 to 72.3% across the planting dates (Fig. 3). The highest C_G (72.3%) was observed for early planting followed by late planting (64.1%). The lowest C_G (43.0%) was recorded for very late planting. The very early planting exhibited an intermediate C_G (57.0%). All the planting dates were statistically different from each other.

H _P varied statistically across planting dates with values ranging between 35.3 and 62.4 cm. H_P declined with delay in planting. H_P was maximum (62.4 cm) for the very early planting date followed by statistically different early (53.3 cm) and late (49.4 cm) planting dates. The lowest H_P (35.3 cm) was observed in very late planting.

MS _N ranged between 3.4 and 4.1 across the different planting dates (Fig. 3). Results indicated a slight decline in MS_N (3.7) with very early planting. MS_N was maximum (4.1 and 4.0) in early and late planting dates, while lowest (3.4) in very late planting.

The results showed that there was a differential response of the four planting dates with respect to L_N with values ranging from 38.2 to 50.8 (Fig. 3). Overall, L_N declined with a delay in planting. A significantly higher L_N (50.8) was noted for the early planting pursued by late planting (45.1). L_N was lowest (38.2) for the very late planting. The results also showed a decline in L_N (42.8) for the very early planting date.

LA _P ranged from 2657 to 5343 cm² across the different planting dates (Fig. 3). LA_P progressed with early planting but declined with delayed in planting. As a result, the highest LA_P (5343 cm²) was noted for early planting succeeded by 4792 cm² LA_P in late planting. The lowest LA_P (2657 cm²) was observed in very late planting. The results also showed that the very early planting date initiated intermediate values for LA_P (4299 cm²).

Effect of planting date on cumulative PAR intercepted and thermal days

PAR _INTC and TD_C varied significantly (P ≤ 0.01) for different planting dates (Table 4). The PAR_INTC ranged between 325 and 816 MJ m^− 2 across the planting dates (Fig. 3). PAR_INTC declined with the delay in planting. PAR_INTC was highest (816 MJ m^− 2) for the early planting date followed by statistically different late planting date (653 MJ m^− 2). The lowest PAR_INTC (325 MJ m^− 2) was noted for very late planting. It was also noted that very early planting also caused a lower PAR_INTC (530 MJ m^− 2).

The TD_C ranged from 46.1 to 59.2 td (Fig. 3) across the planting dates. TD_C values increased with very early (57.5 td) to early planting (59.2 td) and further declined with a delay in planting. The lowest TD_C (46.1 td) was noted for very late planting followed by late planting (52.8 td).

Effect of planting date on tuber yield and yield components

Different planting dates significantly (P ≤ 0.01) affected the tuber yield and yield components (Table 4). T_N ranged from 8.8 to 11.8 across the four planting dates (Fig. 3). T_N values indicated an increasing trend from very early (10.8) to early (11.8) planting and a decreasing trend with a delay in planting. As a result, T_N was lowest (8.8) for the very late planting followed by the late planting (10.1).

Data concerning T_WM displayed a noteworthy contrast across planting dates with values ranging between 66.6 and 103.0 (Fig. 3). T_WM values improved from very early (94.2 g) to early planting (103.0 g) and further declined with delay in planting. T_WM was lowest (66.6 g) in very late planting followed by late planting (88.0 g).

T _YM ranged from 13.3 to 30.7 t ha^− 1 and indicated distinct statistical differences across the planting times (Fig. 3). T_YM values indicated an increasing trend from very early (26.5 t ha^− 1) to early (30.7 t ha^− 1) planting and a decreasing trend with further delay in planting. The lowest T_YM (13.3 t ha^− 1) was recorded for the very late planting followed by the late planting (21.9 t ha^− 1).

T _YU ranged from 2.2 to 3.9 t ha^− 1 across the planting dates (Fig. 3). T_YU values indicated a declining trend from very early (2.5 t ha^− 1) to early (2.2 t ha^− 1) planting and an increasing trend with a delay in planting. T_YU was highest (3.9 t ha^− 1) for the late planting followed by very late planting (3.2 t ha^− 1).

T _YT indicated a wide range (16.5–32.9 t ha^− 1) across the planting dates (Fig. 3). The values for T_YT indicated an increasing trend from very early (29.0 9 t ha^− 1) to early (32.9 9 t ha^− 1) planting and a declining trend with a delay in planting. T_YT was lowest (16.5 t ha^− 1) for the very late planting followed by the late planting (25.8 t ha^− 1).

Effect of genotype on plant phenology and vegetative growth

Analysis of variance revealed presence of significant (P ≤ 0.01) genetic variability for all the traits determining crop phenology and crop vegetative growth (Table 4).

E _D ranged from 10.5 to 24.5 days across the genotypes (Fig. 4). Earliest emergence (10.5 days) was noted for genotype El Beïda followed by Elodie (10.6 days). Delayed E_D was observed for genotype Constance (24.5 days) followed by genotype Désirée (23.8 days). The remaining genotypes recorded intermediate days to plant emergence. All the genotypes were statistically different from each other.

Emergence percentage (E_P) ranged from 83.1 to 100% among the eleven potato genotypes (Fig. 4). E_P was highest (100%) for genotypes Elodie and El Bïeda and both were statistically akin. E_P was lowest (83.1%) for genotype Désirée. The rest of the genotypes (Constance, Fado, Sarpo Mira, Arsenal, Red Valentine, Red Sun, Arizona, and Rock) followed an ascending and statistically different trend with values ranging from (87.20‒96.29%).

Days to plant maturity (PM_D) ranged from 113.9 to 139.2 across the genotypes (Fig. 4). The genotype Elodie had minimum PM_D (113.9 days) followed by statistically different PM_D (117.2 days) noted for genotype El Beïda. In contrast, genotypes Désirée and Fado had the maximum and statistically different values of PM_D (139.2 and 138.4 days, respectively). The remaining genotypes showed intermediate results for PM_D and were placed in ascending order as: Red Sun < Rock < Red Valentine < Arsenal < Arizona < Constance < Sarpo Mira.

C _G ranged between 41.1 and 82.5% among the genotypes (Fig. 4). The maximum C_G (82.5%) was observed for genotype Arizona (65.41%) followed by genotype Constance (66.4%) and both genotypes were statistically different from each other. The lowest C_G (41.1 and 53.2%) was noted for the statistically different genotypes Désirée and Elodie, respectively. Intermediate values of C_G were noted for the remainder of the genotypes including Red Sun (62.6%), Fado (61.4%), Sarpo Mira (60.5%), Red Valentine (57.0%), Rock (56.8%), El Beïda (55.0%), and Arsenal (53.8%).

Genotypic variation indicated wide ranges (35.1–62.3 cm) in H_P (Fig. 4). H_P was highest (62.3 cm) and lowest (35.1 cm) for genotypes Fado and Sarpo Mira, respectively. The remainder of the genotypes (Constance, Elodie, El Beïda, Rock, Arsenal, Désirée, Arizona, Red Sun, and Red Valentine) had intermediate values of H_P (41.94–60.61 cm).

MS _N ranged from 20.8 to 4.5 among the genotypes (Fig. 4). Genotypes Red Valentine and Constance exceeded the rest of genotypes with maximum and statistically different values of MS_N (4.5 and 4.3, respectively). The lowest MS_N was noted for genotypes Sarpo Mira (2.8) and Fado (3.0). However, these genotypes were statistically unique from each other. Almost intermediate MS_N (3.5–4.1) was noticed in the remaining genotypes (Elodie, El Beïda, Arizona. Arsenal, Désirée, Red Sun, and Rock).

Genotypic differences were high for L_N and LA_P with values ranging between 36.1–51.9 and 2766–5837 cm², respectively (Fig. 4). The maximum values of L_N (51.9) and LA_P (5837 cm²) were recorded for genotype Arizona. The genotype Désirée had the least values of L_N (36.1) and LA_P (2766 cm²). These two extremes were interceded by genotypes (Elodie, El Beïda, Arsenal, Rock, Sarpo Mira, Red Valentine, Fado, Red Sun, and Constance).

Effect of genotype on cumulative PAR intercepted and thermal days

There was a significant (P ≤ 0.01) genotypic variation for PAR_INTC and TD_C (Table 4). The PAR_INTC ranged between 382.5 and 962.8 MJ m^− 2 (Fig. 4). The maximum PAR_INTC (962.8 MJ m^− 2) was noted for genotype Arizona followed by statistically different genotype Fado (729.0 MJ m^− 2). The lowest PAR_INTC (382.5 MJ m^− 2) was exhibited by genotype Désirée. The rest of the genotypes recorded intermediate values of PAR_INTC and were ranked in following ascending order: El Beïda < Elodie < Rock < Arsenal < Red Valentine < Constance < Sarpo Mira.

The TD_C ranged from 42.6 to 49.5 td (Fig. 4). The genotype Arizona attained maximum TD_C (49.5 td) among the eleven genotypes. It was followed by statistically different genotype Fado with 48.1 td TD_C. In contrast, genotype Elodie recorded the lowest value of TD_C (42.6 td). The remaining genotypes followed an ascending trend as: Constance < El Beïda < Désirée < Rock < Arsenal < Red Valentine < Red Sun < Sarpo Mira.

Effect of genotype on tuber yield and yield components

Results indicated presence of significant genetic variability (P ≤ 0.01) for all the traits determining tuber yield and yield components (Table 4).

T _N ranged from 7.6 to13.3 among the genotypes (Fig. 4). Highest T_N (13.3) was noted for the genotype Fado, and it was accompanied by statistically different genotype Constance (12.6). Furthermore, the lowest T_N (7.6) was noted for genotype Désirée. T_N interceded among the other genotypes.

T _WM extended from 67.6 to106.5 g among the genotypes (Fig. 4). The maximum values of T_WM were observed in statistically similar genotypes: Arizona (106.5 g), El Beïda (105.7 g) and Elodie (103.3 g). The lowest T_WM (67.6 g) was found for genotype Fado while the remaining genotypes had intermediary values of T_WM (72.5–95.8 g).

The assessment of genotypic divergence disclosed broad fluctuations in T_YM ranging from 18.4 to 29.2 t ha^− 1 (Fig. 4). Genotype Arizona excelled among the genotypes with the highest T_YM (29.2 t ha^− 1) and pursued by statistically dissimilar genotype El Beïda (26.3 t ha^− 1). The smallest T_YM was recorded for genotype Désirée (18.4 t ha^− 1). An intermediary T_YM was noted for the remaining genotypes including Fado (20.6 t ha^− 1), Constance (20.7 t ha^− 1), Arsenal (21.7 t ha^− 1), Red Valentine (22.5 t ha^− 1), Rock (23.0 t ha^− 1), Red Sun (23.3 t ha^− 1), Sarpo Mira (23.6 t ha^− 1), and Elodie (24.9 t ha^− 1).

The T_YU ranged between 1.2 and 5.2 t ha^− 1 (Fig. 4). The genotypes Arizona and El Beïda produced the smallest T_YU (1.2 and 1.3 t ha^− 1, respectively). T_YU was highest (5.2 t ha^− 1) for genotype Fado followed by the statistically different genotype Constance with a 5.0 t ha^− 1 T_YU. The values of T_YU interceded throughout the remaining genotypes.

Assessing the impact of genotype on T_YT showed broad fluctuations in total tube yield ranging from 20.1 to 30.4 t ha^− 1 (Fig. 4). The highest T_YT was found for genotype Arizona (30.4 t ha^− 1) followed by the statistically similar genotypes Red Sun (27.7 t ha^− 1), Elodie (27.6 t ha^− 1), and El Beïda (27.5 t ha^− 1). The smallest T_YT was recorded for genotype Désirée (18.4 t ha^− 1) followed by statistically similar genotypes Arsenal (24.5 t ha^− 1) and Red Valentine (24.6 t ha^− 1). Intermediate values of T_YT were found for the remainder of the genotypes (Constance, Fado, Sarpo Mira, and Rock).

Interactive response of planting date and genotype on plant phenology and vegetative growth

The interactive effects of P×G were highly significant (P ≤ 0.01) on the traits controlling plant phenology and vegetative growth of potato (Table 4). E_D ranged from 9.5‒34.4 days due to the G×E interaction (Fig. 5a). Emergence was accelerated with a delay in planting among all the genotypes. Genotypes Elodie, El Beïda, and Red Sun had a small E_D (9.5 days) for very late planting, while genotype Désirée had the highest E_D (34.4 days) for the very early planting.

E _P ranged from 48.8 to 100% due to P×G interaction (Fig. 5a). Most of the genotypes exhibited the highest E_P for the later planting dates (i.e., late to very late), while the lowest E_P was found for the earlier planting dates (i.e., very early to early). The highest and/or complete E_P (100%) was found for the genotypes Elodie and El Bïeda across all planting dates. Among the genotypes, Constance and Fado had the lowest E_P (48.8 and 51.3%, respectively) for the very early planting date.

PM _D ranged from 97.5 to 153.5 days due to prevalence of P×G interaction (Fig. 5b). PM_D declined with a delay in planting among all genotypes. Among the genotypes, Elodie took minimum days (97.7, 130.5, 119.0, and 113.0 days) to mature across all the four planting dates (i.e., very early, early, late, and very late, respectively). The genotype Désirée had the highest value for PM_D (108 days) for the very late planting.

C _G ranged from 49.2 to 94.7% on account of P×G interaction (Fig. 5a). Plant canopy expanded most in early planting in comparison to other planting dates among all the genotypes. The highest value of C_G (94.7, 90.9, 80.3, and 64.1%) was exhibited by genotype Arizona across all the planting dates (i.e., very early, early, late, and very late, respectively). On the other hand, genotype Désirée had lower values of C_G among the genotypes across planting dates. It had the lowest C_G (28.4%) for the very late planting.

The impact of P×G interaction was visible on H_P with values ranging from 23.9 to 84.0 cm (Fig. 5a). A marked decline in H_P was recorded among all the genotypes with a delay in planting and vice versa. The genotype Red Valentine had the highest H_P (84.0 cm) for the very early planting followed by genotype Fado in both early (67.0 cm) and late (63.3 cm) planting date, and genotype Arizona for the very late planting (43.4 cm). The genotype Sarpo Mira attained lower values of H_P across the four planting dates with lowest H_P (23.9 cm) for the very late planting.

The response of P×G interaction revealed wide range of variation for MS_N (2.5–4.9) (Fig. 5a). Most of the genotypes indicated a differential response to different planting dates. Among the genotypes, Red Valentine had the highest MS_N (4.8–4.9) for the very early to late planting dates, respectively. Most of the genotypes attained lower values of MS_N for the very late planting date. The genotype Sarpo Mira had lower values of MS_N among the genotypes across the four planting dates. It produced the lowest value of MS_N (2.5) in very late planting.

The impact of P×G interaction showed a marked range of variation for L_N (32.0–57.1) (Fig. 5b). For nearly all the genotypes, L_N declined with a delay in planting. The most noteworthy L_N (57.1, 55.2, 52.0, and 43.5) was found for the genotype Arizona for early, late, very early, and very late planting dates, respectively. The genotype Désirée showed smaller values of L_N for all planting dates with the lowest number of leaves (32.0) for very late planting.

LA _P was markedly affected by P×G interactions with values ranging from 1966 to7289 cm² (Fig. 5b). Most of the genotypes obtained higher values of LA_P for the early planting dates. LA_P values declined among the genotypes for the very early and very late planting dates. The genotype Arizona had a high LA_P among the genotypes throughout the planting dates. It produced maximum LA_P (7289 and 7039 cm²) for the early and late planting dates, respectively. Lower values of LA_P were found for the genotype Désirée for all the planting dates with the smallest LA_P (1966 cm²) found for the very late planting.

Interactive response of planting date and genotype on cumulative PAR intercepted and thermal days

The effect of P×G interaction was highly significant (P ≤ 0.01) on PAR_INTC and TD_C (Table 4). PAR_INTC ranged from 109 to 1120 MJ m^− 2 (Fig. 5b). PAR_INTC declined in all the genotypes with a delay in planting. The genotype Arizona attained high value of PAR_INTC throughout the four planting dates. It had the highest PAR_INTC (1120 MJ m^− 2) in early planting followed by late (1057 MJ m^− 2), very early (757 MJ m^− 2), and very late (588 MJ m^− 2) planting. The genotype Désirée had a low PAR_INTC for the majority of the planting dates with the lowest PAR_INTC value (108.7 MJ m^− 2) for the very late planting dates.

TD _C ranged between 40.6 and 69.0 td (Fig. 5b). Nearly all the genotypes indicated a declining trend in TD_C with a delay in planting. The genotype El Beïda achieved high values of TD_C throughout the planting dates. It recorded the maximum TD_C (69.0 td) in very early planting followed by early (63.9 td), late (56.2 td), and very late (47.1 td) planting. The lowest TD_C (40.6 td) was exhibited by genotype Désirée in very late planting among the genotype and planting date treatment combinations.

Interactive response of planting date and genotype on tuber yield and yield components

The impact of P×G interaction was highly significant (P ≤ 0.01) on tuber yield and yield components (Table 4). There was a wide range for T_N (5.3‒15.4) (Fig. 5c). All genotypes showed a decline in T_N with a delay in planting. Genotype Fado had the highest T_N (15.4, 13.7, 13.0, and 11.0) throughout the planting dates. The genotype Désirée had a reduced T_N for most planting dates with minimum T_N (5.3) in very late planting.

Examination of the interaction response of P×G revealed that T_WM ranged from 46.9 to130.6 g (Fig. 5c). Most of the genotypes attained high T_WM values with earlier planting and indicated a declining trend with delayed planting. The maximum and statistically at par T_WM was recorded for genotypes Arizona (130.6 g), Elodie (125.4 g), and El Beïda (124.8 g) in early planting. Least T_WM (46.9 g) was noted for genotype Constance in very late planting.

T _YM ranged from 9.7 to 39.1 t ha^− 1 due to P×G interaction effects (Fig. 5c). T_YM enhanced with early planting date and declined with late and very late planting dates in all the genotypes. All the genotypes produced a greater T_YM for the early planting. Genotype Arizona out-performed the rest of genotypes with highest marketable tuber yield (39.1, 30.8, 29.0, and 17.8 t ha^− 1) in all four planting dates (i.e., early, very early, late, and very late, respectively). Least T_YM (9.7 t ha^− 1) was noted for genotype Désirée in very late planting.

The P×G interaction effects revealed a wide range for T_YU (0.84–8.8 t ha^− 1) as shown by (Fig. 5c). T_YU values indicated an increasing trend with a delay in planting for most of the genotypes. The smallest T_YU (0.84 t ha^− 1) was observed in genotype Arizona followed by genotype El Beïda attaining statistically at par T_YU (0.86 and 0.87 t ha^− 1) in early and very early planting, respectively. The genotype Fado recorded the highest T_YU (8.8 and 5.7 t ha^− 1) for the late and very late planting dates, respectively.

T _YT ranged from 11.7 to 40.0 t ha^− 1 because of P×G interaction (Fig. 5c). The values for T_YT were high for the early planting date and declined with a delay in planting (i.e., late and very late planting dates) for all the genotypes. The genotype Arizona outdid the rest of genotypes by obtaining the highest tuber yield (40.0, 31.9, 30.5, and 19.2 t ha^− 1) for all four planting dates i.e., early, very early, late, and very late planting, respectively. The genotypes Désirée recorded least T_YT (11.7 t ha^− 1) for the very late planting date.

Inter-relationships among the growth and yield determining traits of potato

The examination of the correlation coefficients revealed statistically significant (P ≤ 0.01) associations among the majority of the traits investigated (Fig. 6). There were positive and very strong (r ≥ 0.70) correlations among C_G and L_N, LA_P, and T_N with r values ranging from 0.71–0.96; among PM_D, TD_C, and T_WM (r = 0.78–0.98); between PAR_INTC and C_G, L_N, and LA_P (r = 0.91–0.95).

The results further indicated moderately strong positive correlations (0.30 < r < 0.70) between C_G and H_P, MS_N, PM_D, TD_C, and T_WM (r = 0.33–0.55); between H_P and E_D, L_N, LA_P, PM_D, TD_C, and T_N (r = 0.30–0.56); between L_N and MS_N, PM_D, TD_C, and T_WM (r = 0.41–0.50); between LA_P and MS_N, PM_D, TD_C, and T_WM (r = 0.0.38–0.57); between PAR_INTC and H_P, MS_N, PM_D, TD_C, T_N, and T_WM (r = 0.31–0.66); between T_N and E_D, PM_D (r = 0.31–0.35). The results illustrated very strong negative correlations (r ≥ − 0.70) between E_D and E_P (r = -0.73) and moderately strong negative correlations (r = -0.48) between H_P and E_P.

Additional examination of the data revealed highly significant and very strong (r ≥ 0.70) positive correlations between T_YM or T_YT and most of the traits including C_G, L_N, LA_P, PM_D, TD_C, PAR_INTC, and T_WM with value of r ranging between 0.71 to 0.87 (Fig. 6). As expected, T_YT showed a strong positive relationship with T_YM (r = 0.97). There were moderately strong negative correlations (− 0.40 ≤ r ≤ − 0.70) between T_YU and PM_D, TD_C, TW_M, and TY_M (r = − 0.34 to − 0.51).

Identification of key yield determining traits in potato

Considering our prior results, we observed that determining potato yield is a complicated phenomenon since it depends on several interconnected component traits that regulate crop growth and development. In this part, we attempted to create a method for identifying crucial features that are connected to genotype(s) with higher yield potential across a variety of conditions (i.e., planting dates). Identifying these traits could be helpful in creating a different strategy for increasing potato crop productivity. Therefore, using tuber yield as the dependent variable and the other studied traits as the independent variables, we did a stepwise multiple linear regression for all the traits in two rounds (i.e., forward, and backward selection; cf. materials and methods). This approach resulted into minimum number of key independent traits controlling tuber yield.

The procedure of stepwise regression analysis is elaborated in Table 5. A total of 23 models were tested based on the principles discussed previously. Among these, model Nos. 12, 22, and 23 explained most as well as sufficient variance (R² = 96.3, 94.7, and 94.2%, respectively) in tuber yield (Table 6). In model No. 12, all the traits except L_N, in model No. 22, four traits (E_D, C_G, PAR_INTC, and TD_C), while in model No. 23, only three traits (E_D, C_G, and TD_C) had a significant (P ≤ 0.01) combined effect on tuber yield among a large set of traits. There was a significant increase in each of these traits may lead to an increase in the total tuber yield in potato. This was evident from a close relationship between predicted versus observed tuber yield (data not shown).

Assessing variations in weather conditions

This study investigated the impact of planting dates on weather conditions throughout the growing season. The findings indicate that earlier planting dates (very early and early) generally experienced warmer temperatures (minimum, maximum, and mean air and soil) and higher solar radiation compared to later planting dates (late and very late). Daylength and solar radiation were also higher at the beginning of the season for earlier plantings and increased towards maturity for later plantings. Rainfall patterns varied among planting dates, with earlier plantings receiving more rain at maturity and later plantings receiving more rain mid-season. These variations in weather conditions likely influenced plant growth and development, with some later plantings experiencing negative effects from high temperatures at maturity. The study highlights the importance of considering planting dates in conjunction with weather conditions to optimize crop growth and yield.

Assessing effects of planting date

Assessing the effect of planting time on crop phenology and vegetative growth is crucial for optimizing potato production. We observed significant variations in various phenological and vegetative growth traits across different planting dates, shedding light on the impact of seasonal variability on potato crops. Early plantings (i.e., very early and early planting date) resulted in delayed plant emergence (E_D), while a delay in planting (i.e., late, and very late planting date) advanced the E_D. Such trends might possibly be due to the higher maximum temperatures during early plantings (cf. Figure 1). This aligns with previous findings by MacKerron (1984), Khan (2012), and Khan et al (2019a) which suggest that temperatures higher than optimum (23.4 ± 0.5°C) may lead to delayed tuber sprouting and, consequently, delayed plant emergence. Furthermore, a delay in planting also accelerated plant maturity (PM_D). In other words, later plantings required the least time to mature, which is consistent with the idea that delayed planting can reduce the crop's growth period (Ahmad et al., 2015). Notably, late planting dates experienced high maximum air temperatures at maturity (Fig. 1), which adversely impacted foliage and accelerated maturity. Temperature exerts a significant impact on leaf formation, expansion, and senescence (Kirk and Marshall, 1992; Vos, 1995; Firman et al., 1995; Struik and Ewing, 1995; Van Delden et al., 2001; Fleisher and Timlin, 2006; Fleischer et al., 2006; Struik, 2007)

Temperature changes, whether temporary or ongoing, may alter the morpho-anatomical, physiological, and biochemical processes that are involved in plant growth and development (Wahid et al., 2007). Studies have shown that different planting dates result in distinct climatic conditions and distinct growth durations (Wang et al., 2015). Our results clearly indicated that planting date determines the timing of a crop's phenological stages during the growing season (Sadras and Calderini, 2020).

The extent of green canopy cover (C_G) exhibited a positive correlation with planting date. Early planting resulted in a more substantial C_G in comparison to very late planting. This can be attributed to temperature's influence on branching and leaf expansion, as indicated by Allen and Scott (1980) and Struik et al. (1989). The mean air and soil temperatures as well as daylength and solar radiation were higher for the earlier planting dates initially, contributing to better initial growth conditions. However, late planting dates faced comparatively lower air and soil temperatures along with shorter daylengths and lower solar radiation in the beginning of growing cycle, potentially affecting photosynthetic activity and overall vegetative growth. It was concluded that sub- and/or supra-optimal air temperatures (cf. Figure 1) may negatively affect the expansion of green canopy cover in potato (Struik, 2007).

Both plant height (H_P) and number of mother stems plant^− 1 (MS_N) decreased with delayed planting. Early plantings led to taller plants, possibly due to the influence of higher temperatures during early growth stages (Zhang et al., 2012). As previously mentioned, air temperatures were comparatively higher during early plantings (Fig. 1) that might have enhanced the stem elongation, while lower temperatures during later planting dates can reduce the size of stem which may adversely affect the yield of tubers (Begum et al., 2015). Results concluded that variation in air temperatures due to variable planting dates might have influenced the induction and/or suppression of stem height (Khan et al., 2011). Temperature strongly influences stem elongation and branching (Marinus and Bodlaender, 1975; Allen and Scott, 1980; Struik et al., 1989, Almekinders and Struik, 1994, 1996).

The number of leaves plant^− 1 (L_N) and leaf area plant^− 1 (LA_P) increased with early planting, reflecting the positive impact of higher temperatures and prolonged photoperiods (Wolf et al., 1990; Almekinders and Struik, 1996; Ewing, 1997; Fleisher et al., 2006). Similarly, early planting maximized cumulative PAR intercepted (PAR_INTC) and thermal days (TD_C) due to an extended PM_D with optimal conditions. Conversely, late planting resulted in reduced PAR_INTC and TD_C due to shortened PM_D, indicating the importance of planting date for efficient light interception and heat accumulation.

Planting date significantly influenced tuber yield and its components. Early planting resulted in higher values for mean weight of marketable tuber (T_WM), marketable tuber yield (T_YM), and total tuber yield (T_YT), while later planting dates had a detrimental effect on these crucial parameters. This aligns with previous research by Khan et al. (2011), which highlighted the impact of planting time on tuber production. As discussed previously, a delay in planting led to early plant maturity (PM_D) or shortening of growing period (Kawakami et al., 2005); consequently, plants produced fewer tubers (T_N) due to insufficient PAR_INTC and TD_C, resulting in a reduction in overall plant growth and tuber yield (Struik et al., 1988). On the contrary, early planting initiated delayed plant maturity causing longer growing period and enhanced vegetative growth with higher plant’s capacity to intercept photosynthetically active radiation (PAR_INT) as well as accumulate more thermal days thereby ensuring high tuber yield (Ojeda et al., 2021; Santos et al., 2022).

Understanding the relationship between planting time and crop phenology and growth is essential for sustainable and efficient potato cultivation. Results concluded that planting date optimization could be an important adaptation strategy to cope with seasonal variations and can be effectively used to ensure efficient resource utilization and high crop yield (Li et al., 2022; Wu et al., 2023).

Assessing effects of genotype

The study revealed substantial genotypic variability for traits relevant with plant phenology and vegetative growth, PAR_INTC, TD_C, and tuber yield and yield components (Jones and Allen, 1983). This genotypic variability was mainly due to the differences among genotypes for plant maturity (Fig. 2). For instance, early maturing genotypes such as Elodie, El Bïeda, and Red Sun exhibited lower values of E_D and PM_D, while late maturing genotypes e.g., Désirée, Fado, Sarpo Mira, Constance, and Arizona gave higher values of E_D and PM_D. Most of the late maturing genotypes also exhibited higher values of C_G, L_N, L_AP, PAR_INTC, T_WM, T_YM, and T_YT compared to early maturing genotypes. Kooman and Rabbinge (1996) found that, early maturing potato genotypes tend to allocate a greater proportion of the assimilates available to the tubers at the beginning of the growing season compared to late maturing genotypes; this leads to shorter growing periods and poorer tuber yields as compared to late maturing genotypes.

The improved photosynthetic capability and accumulation of assimilates led to enhanced marketable tuber yield in certain genotypes (Rojoni et al., 2014). For instance, one late maturing genotype Arizona clearly surpassed among the genotypes for vegetative growth and tuber yield determining traits. The yield disparity between the most productive genotype (i.e., Arizona) and least productive genotypes (e.g., Désirée) was 10.3 t ha^− 1 highlighting the significant variation in tuber yield potential. The differences in tuber yield among different potato genotypes (without abiotic or biotic stress) can be analysed by considering the cumulative absorption of light, the efficiency of converting absorbed light energy into biomass, and the allocation of dry matter to the desired plant organ (Khan, 2012). Previous studies by Pashiardis (1987), Spitters (1988), and Van Delden et al. (2001), Khan et al. (2019a,b) have explored these factors.

Our findings emphasize the significant impact of genotype on various aspects of potato growth, development, and yield (Kooman et al.,1996a; Struik and Wiersema, 1999; Pérez et al., 2010; Dash et al., 2018). Genotypic diversity is evident in plant phenology, vegetative growth, PAR interception, thermal requirements, and tuber yield-related traits (Tessema et al., 2020). Understanding these genotype-specific characteristics is crucial for crop management and breeding programs to select and develop potato varieties that best suit specific environmental conditions and production goals. Further research can explore the underlying genetic factors responsible for these variations and identify specific genotypes that exhibit desirable traits for potato cultivation in different agricultural contexts. This knowledge can lead to more targeted breeding efforts and improved potato crop management practices.

Assessing interactive effects of planting date and genotype

The interactive effects of planting date and genotype (i.e., P×G interaction) were found to have a profound influence on the observed traits, offering valuable insights into the adaptability of different potato genotypes to varying seasonal conditions.

Delayed planting generally accelerated E_D across all genotypes. Genotype Elodie displayed the fastest emergence in very late planting, while genotype Désirée had the slowest emergence in very early planting. Time to maturity (PM_D) shortened with delayed planting across all genotypes. Late maturing genotypes (e.g., Désirée, Fado, and Sarpo Mira) consistently required the most time to mature, while early maturing genotypes (e.g., Elodie, El Bïeda, and Red Sun) matured earliest throughout the planting times. This suggests that the responsiveness of genotypes to planting time varies, potentially influenced by their maturity types (Khan et al., 2013) and how they interact with seasonal variations (Struik, 2007). Time to maturity is a genotypic trait that, of course, can be impacted by the date of planting, the climate, and the cultivation techniques used (Abebe et al., 2013; Alemayehu and Jemberie, 2018).

P×G interaction significantly influenced the C_G, with early planting resulting in the most extensive canopy growth across genotypes. Late maturing genotype Arizona consistently exhibited the highest C_G across all the planting dates. These findings emphasize the importance of planting time in modulating canopy development and its impact on potato growth (Allen and Scott, 1992; Schittenhelm et al., 2006). Furthermore, delayed planting led to reduced values of traits including H_P, MS_N, L_N, and LA_P in most of the genotypes and vice versa. Nevertheless, the genotypes exhibited varying responses for these features. For instance, genotype Red Valentine displayed the tallest plants and produced maximum MS_N in very early planting, while genotype Sarpo Mira had the shortest plants and attained lowest MS_N in very late planting. Genotype Arizona consistently had the highest L_N, and LA_P values, while genotype Désirée consistently had lower values. This demonstrates how P×G interactions can influence the physical characteristics of potato plants (Ahmed et al., 2017).

Delayed planting generally resulted in decreased PAR_INTC and TD_C, indicating reduced light interception and lower thermal accumulation, respectively among the genotypes. Late maturing genotypes Arizona consistently exhibited the highest accumulation of PAR_INT and TD_C, respectively across all planting dates. These results underscore the importance of planting time in optimizing light and heat utilization during the growing season. Typically, the amount of PAR_INT by the crop best explains differences in potato yield across environments and genotypes (Allen and Scott, 1980; Kooman et al., 1996a; Schittenhelm et al., 2006).

Later plantings induced a declining trend in tuber yield and yield components among the genotypes. Genotype Arizona consistently outperformed others in terms of T_WM, T_YM, and T_YT across all planting dates and exhibited highest values with early plantings. The relatively better performance of genotype Arizona across the planting dates for majority of the traits compared to other genotypes indicate that it is adapted to diverse growing conditions.

Results further indicated that the ranking of most genotypes also altered across the planting dates for most traits which might be due to various trades-offs between genotype differences for maturity-type and seasonal variation introduced through different planting dates. These findings indicated that there were genotypes that exhibited both specific and generalized adaptations to seasonal variation (Figs. 5a-c). Results concluded that genotype-seasonal specific response underscores the complexity of the genetic regulation of phenological traits and tuber yield production (Miglietta et al., 1998; Hassanpanah et al., 2009; Zakir 2018).

Assessing inter-relationship among traits

The analysis of correlation coefficients among various growth and yield-related traits in potato provides valuable insights into the interrelationships between these traits. These correlations shed light on the factors that contribute to potato growth and tuber yield.

Highly strong relationship between yield and growth attributes such as C_G, L_N, LA_P, PM_D, PAR_INTC, TD_C, and T_WM suggest that these are the critical determinants of yield formation in potato (Khan et al., 2019a,b). These findings align with the established concept that a healthy and extensive canopy contributes to greater photosynthetic activity and, consequently, higher tuber yields. Results also indicated that longer growing periods (delayed maturity) lead to the accumulation of more thermal days and, in turn, larger tuber sizes and enhanced tuber yield. Our results also highlighted the crucial connection between light interception and leaf growth and canopy development. More vegetative growth contributes to PAR_INTC and consequently, increased tuber yields (Ojeda et al., 2021). Results indicated that genotypes or environmental conditions leading to higher production of marketable tubers determines the overall tuber yield.

The findings suggest that there is a trade-off between early maturity and yield in potato cultivation. Early maturity induced by delayed planting and/or genotypes with shorter growing cycle exhibit reduced tuber yields due to limited PAR_INTC and TD_C and smaller T_WM (Santos et al., 2022). Such trade-off emphasizes the need for a careful balance between achieving early maturity for specific market demands and optimizing yield potential through extended growth periods.

In conclusion, the strong correlations among growth and yield-related traits in potato underscore the complex relationships at play in potato cultivation. These traits are influenced by various factors, including genotype and seasonal conditions. Identifying the key traits that consistently contribute to higher yields across different conditions is a challenging but essential task. Such traits play a crucial role in the genetic adaptation of plants to different growth conditions and can be used to develop optimal genotypes for specific producing environments (Kwambai et al., 2024).

Identification of key yield-determining traits in potato

The identification of key yield-determining traits in potato is a crucial step in optimizing crop productivity across diverse conditions. This study employed stepwise multiple linear regression analysis to discern the essential traits associated with higher tuber yields. By doing so, it aimed to establish a comprehensive strategy for enhancing potato crop productivity by prioritizing specific traits.

The findings suggest that a genotype with optimum period of emergence, and high green canopy cover over longer period may contribute to higher values PAR_INTC and TD_C resulting in higher tuber yields in potato crops. Several studies have indicated that canopy cover (Khan et al., 2019a) and PAR_INTC are important determinants of yield differences among the genotypes (Allen and Scott, 1980; Burstall and Harris, 1983; Van Der Zaag and Doornbos, 1987; Kooman et al., 1996a; Khan et al., 2019b).

Our selected traits can serve as valuable indices for selecting genotypes with superior yield potential. By focusing on these specific traits, breeders and growers can develop improved potato varieties that are better suited to various environmental conditions and management practices. This approach paves the way for the creation of a comprehensive selection index that integrates physiological key traits and their interactions (Khan, 2012; Khan et al., 2013). Such an index can streamline the evaluation of potato yield from genetically diverse genotypes across diverse environments. It enables a more targeted and efficient breeding and selection process, ultimately contributing to increased potato crop productivity.

In conclusion, the utilization of stepwise multiple linear regression analysis to identify key yield-determining traits in potato is a robust statistical approach for identifying the most influential traits affecting potato yield. By emphasizing traits that consistently contribute to higher yields, this approach offers practical insights for improving potato cultivation strategies. It aligns with the goal of developing resilient and high-yielding potato varieties capable of meeting global food demands while adapting to changing environmental conditions.

The assessment of potato yield dynamics is a multifaceted endeavor that necessitates the consideration of various factors, including genotype, planting time, and their intricate interactions. This study delves into the contributions of genotype, planting date, and their interplay in shaping potato yield dynamics particularly under sub-tropical growing conditions. Several genotypes were deliberately exposed to seasonal variations created by four different planting dates viz., very early, early, late, and very late. Early planting generally enhanced growth and yield parameters, whereas very late planting negatively affected them. The establishment and productivity of the investigated genotypes exhibited significant differences across seasonal variations. Among the eleven genotypes tested, Arizona achieved the highest tuber yield due to superior growth traits, while Désirée yielded the least. Notably, the planting time-by-genotype interaction revealed that different genotypes responded uniquely to planting dates, altering their rankings and yield-related traits. Key growth traits such as days to emergence, green canopy cover, cumulative PAR interception, and cumulative thermal days were strongly correlated with tuber yield. These traits serve as vital indicators of yield potential and are instrumental in breeding and selection efforts. Early planting can promote canopy growth over longer periods, while late planting may reduce the time to maturity. These temporal variations have substantial repercussions on overall yield potential.

In conclusion, the interplay between genotype and planting time in potato yield dynamics is a multifaceted phenomenon. In an era of climate change and increasing food demand, the contribution of genotype, planting time, and their interactions to potato yield dynamics takes on added significance. The ability to adapt potato cultivation strategies to changing environmental conditions is paramount. Our findings have profound practical implications for potato cultivation under sub-tropical conditions. By understanding how different genotypes respond to varying planting dates and which traits are most influential in determining tuber yield, breeders and growers can make informed decisions. Genotype selection tailored to planting time can enhance yield stability and resilience, ensuring a more reliable food supply.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgements

We thank the staff of Soil Chemistry Section, Directorate of Soils and Plant Nutrition, Agricultural Research Institute, Tarnab, Peshawar, Khyber Pakhtunkhwa, Pakistan as well as Department of Soil and Environmental Sciences, the University of Agriculture, Peshawar, Khyber Pakhtunkhwa, Pakistan for performing the soil analysis. We are also thankful to Pakistan Meteorological Department for providing the weather data. M.S.K. was supported by a grant provided by the Higher Education Commission, Government of Pakistan for Postdoctoral Fellowship.

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Effects of Planting Date and Genotypes on Potato Growth and Yield Determination in a Sub-Tropical Continental Growing Environment

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Abstract

Figures

Introduction

Materials and methods

Site description and experimental field

Plant material

Experimental design and field management

Plant measurements

Calculation of climatic variables

Defining genotype maturity type

Statistical analyses

Results

Assessment of weather conditions

Effect of planting date on phenology and vegetative growth

Effect of planting date on cumulative PAR intercepted and thermal days

Effect of planting date on tuber yield and yield components

Effect of genotype on plant phenology and vegetative growth

Effect of genotype on cumulative PAR intercepted and thermal days

Effect of genotype on tuber yield and yield components

Interactive response of planting date and genotype on plant phenology and vegetative growth

Interactive response of planting date and genotype on cumulative PAR intercepted and thermal days

Interactive response of planting date and genotype on tuber yield and yield components

Inter-relationships among the growth and yield determining traits of potato

Identification of key yield determining traits in potato

Discussion

Assessing variations in weather conditions

Assessing effects of planting date

Assessing effects of genotype

Assessing interactive effects of planting date and genotype

Assessing inter-relationship among traits

Identification of key yield-determining traits in potato

Conclusions

Declarations

Conflict of interest

Acknowledgements

References

Status:

Version 1