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

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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 cm 2 ), 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction 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. Materials and methods 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 ( Y 1 ) and 2018-19 ( Y 2 ). 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 Y 1 and Y 2 , 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 † Y 1 Y 2 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 † Y 1 = 2017-18; Y 2 = 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 ( T 1 ), early: 14 Oct ( T 2 ), late: 26 Oct ( T 3 ), and very-late: 07 Nov ( T 4 ). 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 m 2 ) 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 cm 2 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 m 2 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 m 2 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 m 2 was calculated and transformed to t ha − 1 . Nitrogen (33.3% of total 150 kg ha − 1 as urea), phosphorus (100 kg P 2 O 5 ha –1 as single super phosphate), and potassium (100 kg K 2 O 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 (cm 2 ), 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 ( R 2 ) 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 R 2 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 R 2 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 ( # ) #3 #5 #11 #12 #16 #19 #22 #23 E D + + + + + + + + E P (%) + + + + + + ₋ ₋ C G (%) + + + + + + + + H P (cm) ₋ + + + + + ₋ ₋ MS N ₋ + + + + ₋ ₋ ₋ L N ₋ ₋ ₋ ₋ ₋ ₋ ₋ LA P (cm 2 ) ₋ + + + ₋ ₋ ₋ 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. Results 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 (cm 2 ) 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 cm 2 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 cm 2 ) was noted for early planting succeeded by 4792 cm 2 LA P in late planting. The lowest LA P (2657 cm 2 ) was observed in very late planting. The results also showed that the very early planting date initiated intermediate values for LA P (4299 cm 2 ). 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 cm 2 , respectively (Fig. 4 ). The maximum values of L N (51.9) and LA P (5837 cm 2 ) were recorded for genotype Arizona. The genotype Désirée had the least values of L N (36.1) and LA P (2766 cm 2 ). 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. 5 a). 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. 5 a). 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. 5 b). 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. 5 a). 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. 5 a). 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. 5 a). 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. 5 b). 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 cm 2 (Fig. 5 b). 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 cm 2 ) 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 cm 2 ) 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. 5 b). 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. 5 b). 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. 5 c). 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. 5 c). 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. 5 c). 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. 5 c). 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. 5 c). 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 ( R 2 = 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). Discussion 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. 5 a-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. Conclusions 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. Declarations 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. 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Netherlands Journal of Agricultural Science 36: 11 –22 Struik, PC, Wiersema SG (1999) Seed potato technology. Wageningen, Wageningen Pers. Tessema L, Mohammed W, Abebe T (2020) Evaluation of potato ( Solanum tuberosum L.) varieties for yield and some agronomic traits. Open Agriculture 5: 63–74. Thiele G, Theisen K, Bonierbale M, Walker T (2010) Targeting the poor and hungry with potato science. Potato Journal 37: 75–86. Thiyagu D, Rafii MY, Mahmud TMM, Latif MA, Malek MA, Sentoori G (2013) Genotype by environment assessment in sweetpotato as leafy vegetable using AMMI model. Pakistan Journal of Botany 45: 843–852. UPOV (Union for the Protection of New Varieties of Plants). http://www.upov.int Van Delden A, Kropff MJ, Haverkort AJ (2001) Modeling temperature- and radiation–driven leaf area expansion in the contrasting crops potato and wheat. Field Crops Research 72: 119–142. Van der Zaag DE (1984) Reliability and significance of simple method of estimating the potential yield of potato crop. Potato Research 27: 51–73. Van der Zaag DE, Doornbos JH (1987) An atempt to explain differences in the yielding ability of potato cultivars based on differences in cumulative light interception, utilization efficiency of foliage and harvest index. Potato Research 30: 551–568. Vos J (1995) Foliar development of the potato plant and modulations by environmental factors. In: Kabat P, Van den Broek BJ, Marshall B, Vos J (Eds.), Modelling and parameterization of the soil-plant-atmosphere system. A comparison of potato growth models. Wageningen Pers, Wageningen, pp. 21–38. Wang CL, Shen SH, Zhang S., Li QZ, Yao YB (2015) Adaptation of potato production to climate change by optimizing sowing date in the Loess Plateau of central Gansu, China. Journal of Integrative Agriculture 14: 398–409. Wolf S, Marani A, Rudich J (1990) Effects of temperature and photoperiod on assimilate partitioning in potato plants. Annals of Botany 66: 513–520. Wu F, Guo S, Huang W, Han Y, Wang Z, Feng L, Wang G, Li X, Lei Y, Yang B, Xiong S, Zhi X, Chen J, Xin M, Wang Y, Li Y (2023) Adaptation of cotton production to climate change by sowing date optimization and precision resource management. Industrial Crops and Products 203: 117167. Yin X, Kropff MJ, McLaren G, Visperas RM (1995) A nonlinear model for crop development as a function of temperature. Agricultural and Forest Meteorology 77: 1–16. Yin X, Struik PC, Tang J, Qi C, Liu T (2005) Model analysis of flowering phenology in recombinant inbred lines of barley. Journal of Experimental Botany 56: 959–965. Zakir M (2018). Review on genotype × environment interaction in plant breeding and agronomic stability of crops. Journal of Biology, Agriculture and Healthcare 8: 14–21. Zhang K, Wang RY, Li QZ, Zhao H, Wang HL, Guo L, Zhang XY (2012) Effects of sowing date on the growth and tuber yield of potato in semi-arid area of loess plateau in central Gansu Province of Northwest China. Chinese Journal of Ecology 31: 2261–2268. Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2024 Read the published version in Potato Research → Version 1 posted Editorial decision: Minor revisions 30 Sep, 2024 Reviewers agreed at journal 18 Jul, 2024 Reviewers invited by journal 11 Jul, 2024 Editor assigned by journal 10 Jul, 2024 First submitted to journal 10 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4720912","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325489363,"identity":"d1f11db9-d3cc-423a-b97c-4cbed8b7ca63","order_by":0,"name":"Muhammad Sohail Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFACHgaGhAIQg/kAkJCQIVKLAYjBlgDSwkOcFgawFh4DGBc/kG/vPfjhgQFD4obbZz6/ulFjwcPAfvjoBnxaDM6cS5ZIAGk5l7vNOucY0GE8aWk38GqRyDEAacndcIZ3m3EOG1CLBI8ZXi3yM3KMf0C08DwzzvlHhBaGGzlmUFt4mB/nthGhxeDMGTOLBAOJ+pln2MyYc/skeNgI+UW+vcf45o8KG2O+M8yPP+d8q5PjZz98DL/DIEACRLBBSCKUwwHzB1JUj4JRMApGwcgBAIeZQ2UWeMgoAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0849-3765","institution":"Gomal University Faculty of Agriculture","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Sohail","lastName":"Khan","suffix":""},{"id":325489364,"identity":"8b1d5478-32c7-446b-8ae2-75c40b3c5d69","order_by":1,"name":"Gerrit Hoogenboom","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Gerrit","middleName":"","lastName":"Hoogenboom","suffix":""},{"id":325489365,"identity":"b116e743-fd86-4eb1-a6a7-011b2a8a4743","order_by":2,"name":"Syeda Mehwish Gillani","email":"","orcid":"","institution":"Gomal University Faculty of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Syeda","middleName":"Mehwish","lastName":"Gillani","suffix":""},{"id":325489366,"identity":"23a62563-92d6-44ff-9aac-e41aaffcb87e","order_by":3,"name":"Alam Syed Shah","email":"","orcid":"","institution":"Gomal University Faculty of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Alam","middleName":"Syed","lastName":"Shah","suffix":""},{"id":325489367,"identity":"5c461980-8ad8-4eef-8b74-537f607a7ce2","order_by":4,"name":"Ilham Khan","email":"","orcid":"","institution":"Gomal University Faculty of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ilham","middleName":"","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2024-07-11 00:06:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4720912/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4720912/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11540-024-09833-x","type":"published","date":"2024-12-18T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61764480,"identity":"f3dbcd00-ea22-46b1-802f-0d98bfec8225","added_by":"auto","created_at":"2024-08-05 10:11:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52114,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum air temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eMEAN_AIR\u003c/sub\u003e), minimum air temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eMAX_AIR\u003c/sub\u003e), mean air temperature\u003cem\u003e \u003c/em\u003e(\u003cem\u003eT\u003c/em\u003e\u003csub\u003eMEAN_AIR\u003c/sub\u003e), mean soil temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eMEAN_SOIL\u003c/sub\u003e), daylength (\u003cem\u003eDAYL\u003c/em\u003e),\u0026nbsp; cumulative\u0026nbsp; solar radiation (\u003cem\u003eSRAD\u003c/em\u003e), and rainfall (\u003cem\u003eRAIN\u003c/em\u003e) during the growing season (at 30-day intervals from planting until plant maturity “M”) of four individual planting dates: VE (very early: 02 Oct), E (early: 14 Oct), L (late: 26 Oct), and VL (very late: 07 Nov).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4720912/v1/e4058d326c955323522a137d.png"},{"id":61764485,"identity":"139727c7-2108-444e-8b37-28ac38da49ed","added_by":"auto","created_at":"2024-08-05 10:11:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38574,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic representation of cluster analysis for plant maturity (\u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e) within eleven genotypes. The dashed line shows hypothetical intersection of four main clusters for the selection of four maturity classes (very early (VE), early (E), late (L), and very late (VL)). For the acronyms of genotypes, see Table 2.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4720912/v1/e4f1718717cc5113a7f25ac7.png"},{"id":61764482,"identity":"67f252d4-8747-41d3-8b18-5d206e825670","added_by":"auto","created_at":"2024-08-05 10:11:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55688,"visible":true,"origin":"","legend":"\u003cp\u003eMean values of different traits of potato (across eleven genotypes) for four individual planting times. For the acronyms of traits, see Table 3.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4720912/v1/ce5cf46e7a3c602364ec73c4.png"},{"id":61765121,"identity":"2345e953-d1ec-44df-98f5-e77072f86046","added_by":"auto","created_at":"2024-08-05 10:19:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69423,"visible":true,"origin":"","legend":"\u003cp\u003eMean values of different traits of potato (across four planting times) for eleven genotypes. For the acronyms of genotypes and traits, see Tables 2 and 3, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4720912/v1/62be9e65ff85b72e0ad7b5f9.png"},{"id":61764484,"identity":"317140d6-3665-48f7-8b26-20210dfa642e","added_by":"auto","created_at":"2024-08-05 10:11:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":261987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e5a \u003c/strong\u003eEffect of planting date-by-genotype (\u003cem\u003eP\u003c/em\u003e×\u003cem\u003eG\u003c/em\u003e) interaction on different traits of potato. Where, VE, E, L, VL represent four planting dates i.e., very early (02 Oct), early (14 Oct), late (26 Oct), and very late (07 Nov), respectively.\u003csub\u003e \u003c/sub\u003eFor the acronyms of traits, see Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5b \u003c/strong\u003eEffect of planting date-by-genotype (\u003cem\u003eP\u003c/em\u003e×\u003cem\u003eG\u003c/em\u003e) interaction on different traits of potato. Where, VE, E, L, VL represent four planting dates i.e., very early (02 Oct), early (14 Oct), late (26 Oct), and very late (07 Nov), respectively. For the acronyms of traits, see Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5c \u003c/strong\u003eEffect of planting date-by-genotype (\u003cem\u003eP\u003c/em\u003e×\u003cem\u003eG\u003c/em\u003e) interaction on different traits of potato. Where, VE, E, L, VL represent four planting dates i.e., very early (02 Oct), early (14 Oct), late (26 Oct), and very late (07 Nov), respectively. For the acronyms of traits, see Table 3.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4720912/v1/9d0efe2779fd12998c2912ad.png"},{"id":61764483,"identity":"739728c5-c3c8-4622-9976-eb937370b503","added_by":"auto","created_at":"2024-08-05 10:11:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":48011,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map revealing correlations among growth and yield determining traits of potato. Where (1) \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, (2) \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, (3) \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, (4) \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, (5) \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, (6) \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, (7) \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, (8) \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, (9) \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e, (10) \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, (11) \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, (12) \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e, (13) \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e, (14) \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e, and (15) \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT. \u003c/sub\u003eFor the acronyms of traits, see Table 3.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4720912/v1/8f94d946fd5fa8355a8a2920.png"},{"id":72202052,"identity":"006c58c0-f0d6-44c6-97fa-3cecc92e37d2","added_by":"auto","created_at":"2024-12-23 16:14:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2264149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4720912/v1/421c0f4a-81e6-433b-9d8c-7866e60a32ef.pdf"}],"financialInterests":"","formattedTitle":"Effects of Planting Date and Genotypes on Potato Growth and Yield Determination in a Sub-Tropical Continental Growing Environment","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePotato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.) is a major food crop worldwide (Calıskan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), 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., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). 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, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Hamm and Monique, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Thiele et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Devaux et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). 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., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Devaux et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe global demand for potatoes is increasing due to its agronomical plasticity and ability to thrive in diverse agro-climatic conditions (Haverkort, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Haverkort and Struik, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). 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, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Raymundo et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Eid et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Closing the potato production gap is therefore crucial for preserving food supplies for the foreseeable future (Calıskan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePotato tuber yield production is the result of multiple plant functions that are influenced by environmental factors and genotype (Kooman et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1996a\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003eb\u003c/span\u003e). The seasonal variations in a particular area may directly affect plant phenology thereby affecting the potential crop productivity (Samberg et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). 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., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). 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, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Squire, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Furthermore, the interactive response of a developing plant to seasonal changes is very complex. For instance, a 5 \u0026ordm;C increase in optimum temperature (20 \u0026ordm;C) for photosynthesis may result in a 25% reduction in the photosynthesis rate (Burton, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) 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, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Furthermore, tuber yield in potato is a complex trait and considered polygenic in nature, displaying high variations (Bradshaw et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and genotype-by-environment (G\u0026times;E) interactions (Bombik et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rymuza and Bombik, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Flis et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bradshaw, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering 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\u0026times;E interaction (Thiyagu et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). 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., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Koemel et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). 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., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e \u003cp\u003eNumerous 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., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Seifert et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Dornbusch et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Paz et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). 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., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Arab et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Darabi and Omidvari, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eTherefore, 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.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSite description and experimental field\u003c/h2\u003e \u003cp\u003eTwo 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 (\u003cem\u003eY\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e) and 2018-19 (\u003cem\u003eY\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e). The experimental field is located between latitude 31\u0026ordm; 43' 27.04'' N, longitude 70\u0026ordm; 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., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). 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 \u003cem\u003eRabi\u003c/em\u003e) starting from October‒March with a mean maximum air temperature of 24 \u0026ordm;C (\u0026plusmn;\u0026thinsp;5.5) and a summer-season (locally called \u003cem\u003eKharif\u003c/em\u003e) starting from April‒September with a mean maximum air temperature of 36 \u0026ordm;C (\u0026plusmn;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Daily global solar radiation values were calculated from the daily sunshine hours during both the experimental years following the \u0026Aring;ngstr\u0026ouml;m method (\u0026Aring;ngstr\u0026ouml;m, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1924\u003c/span\u003e). The soil temperature at mid-day was recorded daily during the growing season using a digital soil thermometer (Rapitest, USA).\u003c/p\u003e \u003cp\u003eField soil samples at a 0\u0026thinsp;~\u0026thinsp;30 cm depth were collected from three random spots of the experimental field to determine the soil physical and chemical characteristics two weeks\u0026rsquo; prior to the start of the experiments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 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. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). 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 (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). 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\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. 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\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and was marginal in phosphorus (1.9‒1.8 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in \u003cem\u003eY\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e and \u003cem\u003eY\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, respectively. The soil series of the experimental site was Zindani, and the soil class was Typic Torrifluent (Soil Survey Staff, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical and chemical characteristics of the topsoil layer (0\u0026ndash;30 cm) for the two experimental years.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eExperimental year\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB-DTPA extractable P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB-DTPA extractable K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edS m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTextural class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSandy loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSandy loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003cem\u003eY\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2017-18; \u003cem\u003eY\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2018-19\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePlant material\u003c/h2\u003e \u003cp\u003eEleven contrasting potato genotypes i.e., Elodie, El Be\u0026iuml;da, Red Sun, Red Valentine, Rock, Arsenal, Constance, Arizona, Sarpo Mira, D\u0026eacute;sir\u0026eacute;e, and Fado, were selected for this research. These genotypes were chosen for their pronounced variability in plant traits. The genotype D\u0026eacute;sir\u0026eacute;e 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\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOrigin and background history of planting material.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerial No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcronym\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrigin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBreeding company\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkin colour\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElodie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWhite to Yellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEl Be\u0026iuml;da\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTriskalia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePale Yellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed Sun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDe Nijs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed Valentine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDe Nijs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeijer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArsenal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgrico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgrico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArizona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgrico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u0026eacute;sir\u0026eacute;e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHZPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSarpo Mira\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe United Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSarpo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePink\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFado\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScotland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe James Hutton Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design and field management\u003c/h2\u003e \u003cp\u003eThe 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 (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e), early: 14 Oct (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e), late: 26 Oct (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e), and very-late: 07 Nov (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e). The sub-plots consisted of eleven different potato genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 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\u0026times;11 genotypes\u0026times;03 replicates). Each plot (16.2 m\u003csup\u003e2\u003c/sup\u003e) had 6 ridges with 12 plants each. Soil preparation was performed with a disk plough, rotavator, and cultivator.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of traits and their methods of determination.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcronym\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMethod of determination\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays to 50% plant emergence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e‒\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe period (days) required for attainment of fifty percent plant emergence were counted after planting of clones.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant emergence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThis was quantified using following formula as:\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{\\text{P}}=\\:\\frac{\\text{Number\\:of\\:plants\\:emerged}}{\\text{Sum\\:of\\:clones\\:planted\\:}}\\:\\times\\:100\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen canopy cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe green canopy cover was visually observed on bi-weekly basis by using a grid as described by Burstall and Harris (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) and maximum value was estimated.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThis was recorded at the end of crop cycle with measuring stick.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of mother stems plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e‒\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThis was recorded at the end of crop cycle.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of leaves\u003c/p\u003e \u003cp\u003eplant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e‒\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAggregate of leaves appeared during the crop cycle were recorded.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeaf area plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThis was obtained by dividing the average leaf area (\u003cem\u003eLA\u003c/em\u003e) of the plant by the total number of leaves on the plant. \u003cem\u003eLA\u003c/em\u003e was quantified following Omolaiye et al. (2015) as:\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LA=0.41(L\\times\\:W\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eL\u003c/em\u003e and \u003cem\u003eW\u003c/em\u003e stand for the maximum lamina length and width, respectively.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays to plant maturity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e‒\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThis was determined by counting the total period (days) from \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e to attainment of physiological maturity (i.e., 90% of the canopy senescence).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCumulative PAR intercepted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e was converted to PAR-interception percentage following Burstall and Harris (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Daily values of \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINT\u003c/sub\u003e were summed to obtain \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCumulative thermal days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003etd\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe approach of Yin et al. (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Khan et al. (2019) was used for conversion of the actual days into thermal days (\u003cem\u003eTD\u003c/em\u003e). Daily values of \u003cem\u003eTD\u003c/em\u003e were summed to obtain \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of tubers plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e‒\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal tubers formed were counted at plant maturity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean weight of marketable tuber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean tuber weight (\u0026ge;\u0026thinsp;35 mm) was found by using analytical balance.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarketable tuber yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTuber (\u0026ge;\u0026thinsp;35 mm) yield obtained from the plot area of 4.05 m\u003csup\u003e2\u003c/sup\u003e was calculated and transformed to t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarketable tuber yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTuber (\u0026lt;\u0026thinsp;35 mm) yield obtained from the plot area of 4.05 m\u003csup\u003e2\u003c/sup\u003e was calculated and transformed to t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal tuber yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTuber yield from the plot area of 4.05 m\u003csup\u003e2\u003c/sup\u003e was calculated and transformed to t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNitrogen (33.3% of total 150 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as urea), phosphorus (100 kg P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e as single super phosphate), and potassium (100 kg K\u003csub\u003e2\u003c/sub\u003eO ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e 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.\u003c/p\u003e \u003cp\u003eHealthy 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 (\u0026plusmn;\u0026thinsp;20) mm of irrigation during both the growing seasons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePlant measurements\u003c/h2\u003e \u003cp\u003eObservations 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\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, number of mother stems plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, leaf area plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (cm\u003csup\u003e2\u003c/sup\u003e), 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\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, mean weight of marketable tuber (g), total tuber yield (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and marketable tuber yield (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) 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\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of climatic variables\u003c/h2\u003e \u003cp\u003eThe cumulative intercepted photosynthetically active radiation (\u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e, MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) for the whole growth period was estimated following the approach of Khan (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Khan et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). The percentage green canopy cover (\u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e) were transformed into percentage of PAR intercepted (\u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINT\u003c/sub\u003e), using the linear equation developed by Burstall and Harris (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), which is \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINT\u003c/sub\u003e (%)\u0026thinsp;=\u0026thinsp;0.956 \u0026times; canopy cover (%) \u0026ndash; 4.95. The daily values of \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINT\u003c/sub\u003e were added together to calculate the \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e. The daily incident PAR was determined as 50% of the total global solar radiation according to Spitters (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). 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, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The procedure of beta thermal time proposed by Yin et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) was adopted for the calculation of thermal days (\u003cem\u003etd\u003c/em\u003e). This method is more resilient and takes into consideration the non-linear correlation between temperature (\u003cem\u003eT\u003c/em\u003e) and the rate of growth or development (\u003cem\u003eg\u003c/em\u003e(\u003cem\u003eT\u003c/em\u003e)), which is determined by three cardinal temperatures: the base temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eb\u003c/sub\u003e), the optimal temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e), and the ceiling temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003ec\u003c/sub\u003e). The temperature estimates of \u003cem\u003eT\u003c/em\u003e\u003csub\u003eb\u003c/sub\u003e = 5.5 ˚C, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eo\u003c/sub\u003e = 23.4 ˚C, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003ec\u003c/sub\u003e = 34.6 ˚C were employed based on the research conducted by Khan (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Khan et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003eb\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDefining genotype maturity type\u003c/h2\u003e \u003cp\u003eThe 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 (\u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e). 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. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eBased on a preliminary analysis of the data, it was found that the interactions between the experimental year (\u003cem\u003eY\u003c/em\u003e), planting date (\u003cem\u003eP\u003c/em\u003e), and genotypes (\u003cem\u003eG\u003c/em\u003e) were not statistically significant, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Therefore, the data from the two experimental years were combined and analyzed using a general analysis of variance, considering main effects of \u003cem\u003eP\u003c/em\u003e, \u003cem\u003eG\u003c/em\u003e, and their interactions (\u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e). 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 (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) was included in the model, while the others were excluded (James et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Peter and Bruce, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This strategy was implemented until the point where the value of \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e 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 \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e 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., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStepwise regression analysis of different traits using total tuber yield \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) as a dependent variable.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrait\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eSelected model number (\u003cem\u003e#\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e#5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e#11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e#12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e#16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e#19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e#22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e#23\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e (MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (\u003cem\u003etd\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e₋\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 **\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e65.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e69.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e96.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e94.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e94.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e**\u003c/sup\u003e Significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e The \u0026lsquo;+\u0026rsquo; symbol denotes the independent trait whose mean value was included in the regression model, \u0026lsquo;-\u0026rsquo; symbol denotes the independent trait whose mean value was exempted from the regression model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eWhere \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e = Days to 50% plant emergence, \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e = Plant emergence, \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e = Green canopy cover, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e = Plant height, \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e = Number of mother stems plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e = Number of leaves plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e = Leaf area plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e = Days to plant maturity, \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e = Cumulative PAR intercepted, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e = Cumulative thermal days, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e = Number of tubers plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e = Mean weight of marketable tuber.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of weather conditions\u003c/h2\u003e \u003cp\u003eThe weather conditions created by different planting dates are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The earlier planting date exhibited a broader range of values in most of the meteorological parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mean minimum air temperature was comparatively higher in the earlier than in the later planting dates from 0\u0026ndash;29 DAP, 30\u0026ndash;59 DAP, and 60\u0026ndash;89 DAP, and was higher in the later than in the earlier planting dates from 90 DAP until plant maturity.\u003c/p\u003e \u003cp\u003eThe \u0026micro;ean \u0026micro;ini\u0026micro;u\u0026micro; air te\u0026micro;perature was highest (18.9\u0026deg;C) for very early planting date followed by early planting date (17.2\u0026deg;C) at the start of growing season (0\u0026ndash;29 DAP), dropped sharply to 5.8\u0026deg;C and 6.0\u0026deg;C at 90\u0026ndash;119 DAP, started rising again from 120 DAP until plant maturity (9.3\u0026deg;C and 11.6\u0026deg;C, respectively). In case of late and very late planting dates, the mean minimum air temperature started at 14.9\u0026deg;C and 12.7\u0026deg;C in the beginning of growing season (0\u0026ndash;29 DAP), decreased to 6.2\u0026deg;C and 5.9\u0026deg;C at 60\u0026ndash;89 DAP and then started rising again from 90 DAP (20.8\u0026deg;C) peaking at 12.7\u0026deg;C and 14.3\u0026deg;C, respectively at maturity.\u003c/p\u003e \u003cp\u003eThe \u0026micro;ean \u0026micro;axi\u0026micro;u\u0026micro; air te\u0026micro;perature was highest (28.8\u0026deg;C) for very early planting date at the start, decreased steadily to 16.8\u0026deg;C at 90\u0026ndash;119 DAP, then rose to 20.8\u0026deg;C at plant maturity. In case of early and late planting dates, the mean maximum air temperature started at 25.5\u0026deg;C and 22.6\u0026deg;C decreased to 16.7\u0026deg;C at 60\u0026ndash;89 DAP and then peaked at 22.7\u0026deg;C and 24.4\u0026deg;C, respectively at maturity. In case of very late planting date, mean maximum air temperature was 21.1\u0026deg;C in the\u003c/p\u003e \u003cp\u003ebeginning dropped to 17.5\u0026deg;C at 60\u0026ndash;89 DAP and then started rising at 90\u0026ndash;119 DAP (20.8\u0026deg;C) peaking at 27.4\u0026deg;C at plant maturity.\u003c/p\u003e \u003cp\u003eThe \u0026micro;ean air te\u0026micro;perature was highest (24.0\u0026deg;C) for very early planting date at the start (0\u0026ndash;29 DAP), declined to 11.3\u0026deg;C at 90\u0026ndash;119 DAP, then rose to 15.1\u0026deg;C at plant maturity. In case of early planting date, the mean air temperature started at 21.4\u0026deg;C decreased to 11.5\u0026deg;C at 60\u0026ndash;89 DAP and then peaked to 17.1\u0026deg;C at maturity. In case of late and very late planting dates, mean air temperature started lowest at 18.9\u0026deg;C and 17.0\u0026deg;C, dipped to 11.4\u0026deg;C and 11.7\u0026deg;C at 60\u0026ndash;89 DAP and then peaked to 18.7\u0026deg;C and 20.8\u0026deg;C, respectively at maturity.\u003c/p\u003e \u003cp\u003eThe \u0026micro;ean soil te\u0026micro;perature was highest (22.0\u0026deg;C) for very early planting date at the start (0\u0026ndash;29 DAP), declined to 10.9\u0026deg;C at 90\u0026ndash;119 DAP, then rose to 14.3\u0026deg;C at plant maturity. In case of early planting date, the mean soil temperature started at 20.3\u0026deg;C and decreased to 10.2\u0026deg;C at 60\u0026ndash;89 DAP and then peaked to 16.2\u0026deg;C, respectively at plant maturity. In case of late and very late planting dates, mean soil temperature started lowest at 17.0\u0026deg;C and 15.1\u0026deg;C, dipped to 10.0\u0026deg;C and 9.8\u0026deg;C at 60\u0026ndash;89 DAP and then peaked to 17.0\u0026deg;C and 19.9\u0026deg;C at maturity, respectively.\u003c/p\u003e \u003cp\u003eThe daylength was maximum (11:10 and 10.48 hours) in the beginning (0\u0026ndash;29 DAP) and decreased sharply to 09:57 hours at 60\u0026ndash;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.\u003c/p\u003e \u003cp\u003eThe 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\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) followed by very early planting date (562.2 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) in the start of growing season (0\u0026ndash;29 DAP). The solar radiation decreased gradually at 90\u0026ndash;119 DAP (492.0 and 478.8 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and later peaked at maturity (596.7 and 497.3 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) in both planting dates, respectively. The late and very late planting dates accumulated comparatively lower solar radiation (530.1 and 514.4 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) in the beginning of growing season (0\u0026ndash;29 DAP), later fluctuated and then dropped significantly to 353.8 and 138.6 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 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\u0026deg;C) had a detrimental effect on foliage resulting into early plant maturity and reduction in accumulated solar radiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEffect of planting date on phenology and vegetative growth\u003c/h2\u003e \u003cp\u003ePlanting date had a significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) impact on all the traits determining plant phenology and vegetative growth of potato (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance in \u003cem\u003eF\u003c/em\u003e values of potato traits among two growing seasons, four planting dates, and eleven potato genotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental year (\u003cem\u003eY\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e325.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.17*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e16942.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3258.95**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1178.58**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanting date (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35407.07**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460.78**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10969.09**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8499.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e981.77**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype (\u003cem\u003eG\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11838.95**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.09**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2685.42**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1845.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e929.06**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026times;G\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026times;P\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u0026times;G\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1565.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.01**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e152.11**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129.99**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e157.71**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026times;P\u0026times;G\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eL\u003c/b\u003e\u003csub\u003e\u003cb\u003eN\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLA\u003c/b\u003e\u003csub\u003e\u003cb\u003eP\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(cm\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003eD\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePAR\u003c/b\u003e\u003csub\u003e\u003cb\u003eINTC\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(MJ m\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTD\u003c/b\u003e\u003csub\u003e\u003cb\u003eC\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(\u003c/b\u003e\u003cb\u003etd\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental year (\u003cem\u003eY\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1715.64**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1167.69**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e325.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2264.32**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.55**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanting date (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2311.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1450.12**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.175E\u0026thinsp;+\u0026thinsp;05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53873.67**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11766.24**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype (\u003cem\u003eG\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e528.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e11838.95**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8943.67**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1789.67**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026times;G\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026times;P\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u0026times;G\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.98**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1565.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1426.68**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e303.80**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026times;P\u0026times;G\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eN\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eWM\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(g)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eYM\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(t ha\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eYU\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(t ha\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eYT\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(t ha\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental year (\u003cem\u003eY\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e471.48**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10530.11**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e255.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12834.64**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanting date (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e439.37**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351.53**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e17705.45**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1941.98**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17136.20**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype (\u003cem\u003eG\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270.57**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1017.73**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2269.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e856.13**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026times;G\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026times;P\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u0026times;G\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.92**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.83**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e59.37**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204.4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.80**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026times;P\u0026times;G\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e˗\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e**\u003c/sup\u003e Significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01; \u003csup\u003e*\u003c/sup\u003e Significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05; ˗ Non-significant\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eWhere \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e = Days to 50% plant emergence, \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e = Plant emergence, \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e = Green canopy cover, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e = Plant height, \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e = Number of mother stems plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e = Number of leaves plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e = Leaf area plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e = Days to plant maturity, \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e = Cumulative PAR intercepted, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e = Cumulative thermal days, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e = Number of tubers plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e = Mean weight of marketable tuber, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e = Marketable tuber yield, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e = Unmarketable tuber yield, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e = Total tuber yield.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePlants required 12.0 to 23.3 days for emergence (\u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e) as a resultant of different planting dates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e advanced with a delay in planting and vice versa. Minimum \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e (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.\u003c/p\u003e \u003cp\u003e \u003cem\u003eE\u003c/em\u003e \u003csub\u003eP\u003c/sub\u003e ranged between 73.8 and 99.2% across different planting dates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e increased for the later planting dates. The highest and statistically similar \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (99.2 and 98.4%) was observed for the very late and late planting dates, respectively. \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e was smallest (73.8%) for the very early planting succeeded by early planting (95.1%). However, both planting dates were statistically different.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePM\u003c/em\u003e \u003csub\u003eD\u003c/sub\u003e varied across the planting dates and ranged from 115 to 134 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003e \u003cem\u003eC\u003c/em\u003e \u003csub\u003eG\u003c/sub\u003e ranged from 43.0 to 72.3% across the planting dates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e (72.3%) was observed for early planting followed by late planting (64.1%). The lowest \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e (43.0%) was recorded for very late planting. The very early planting exhibited an intermediate \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e (57.0%). All the planting dates were statistically different from each other.\u003c/p\u003e \u003cp\u003e \u003cem\u003eH\u003c/em\u003e \u003csub\u003eP\u003c/sub\u003e varied statistically across planting dates with values ranging between 35.3 and 62.4 cm. \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e declined with delay in planting. \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e 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 \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (35.3 cm) was observed in very late planting.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMS\u003c/em\u003e \u003csub\u003eN\u003c/sub\u003e ranged between 3.4 and 4.1 across the different planting dates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Results indicated a slight decline in \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (3.7) with very early planting. \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e was maximum (4.1 and 4.0) in early and late planting dates, while lowest (3.4) in very late planting.\u003c/p\u003e \u003cp\u003eThe results showed that there was a differential response of the four planting dates with respect to \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e with values ranging from 38.2 to 50.8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e declined with a delay in planting. A significantly higher \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (50.8) was noted for the early planting pursued by late planting (45.1). \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e was lowest (38.2) for the very late planting. The results also showed a decline in \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (42.8) for the very early planting date.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLA\u003c/em\u003e \u003csub\u003eP\u003c/sub\u003e ranged from 2657 to 5343 cm\u003csup\u003e2\u003c/sup\u003e across the different planting dates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e progressed with early planting but declined with delayed in planting. As a result, the highest \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (5343 cm\u003csup\u003e2\u003c/sup\u003e) was noted for early planting succeeded by 4792 cm\u003csup\u003e2\u003c/sup\u003e \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e in late planting. The lowest \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (2657 cm\u003csup\u003e2\u003c/sup\u003e) was observed in very late planting. The results also showed that the very early planting date initiated intermediate values for \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (4299 cm\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffect of planting date on cumulative PAR intercepted and thermal days\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePAR\u003c/em\u003e \u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e varied significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) for different planting dates (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e ranged between 325 and 816 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e across the planting dates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e declined with the delay in planting. \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e was highest (816 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) for the early planting date followed by statistically different late planting date (653 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). The lowest \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e (325 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) was noted for very late planting. It was also noted that very early planting also caused a lower \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e (530 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e ranged from 46.1 to 59.2 \u003cem\u003etd\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e) across the planting dates. \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e values increased with very early (57.5 \u003cem\u003etd\u003c/em\u003e) to early planting (59.2 \u003cem\u003etd\u003c/em\u003e) and further declined with a delay in planting. The lowest \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (46.1 \u003cem\u003etd\u003c/em\u003e) was noted for very late planting followed by late planting (52.8 \u003cem\u003etd\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEffect of planting date on tuber yield and yield components\u003c/h2\u003e \u003cp\u003eDifferent planting dates significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) affected the tuber yield and yield components (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e ranged from 8.8 to 11.8 across the four planting dates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e 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, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e was lowest (8.8) for the very late planting followed by the late planting (10.1).\u003c/p\u003e \u003cp\u003eData concerning \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e displayed a noteworthy contrast across planting dates with values ranging between 66.6 and 103.0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e values improved from very early (94.2 g) to early planting (103.0 g) and further declined with delay in planting. \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e was lowest (66.6 g) in very late planting followed by late planting (88.0 g).\u003c/p\u003e \u003cp\u003e \u003cem\u003eT\u003c/em\u003e \u003csub\u003eYM\u003c/sub\u003e ranged from 13.3 to 30.7 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and indicated distinct statistical differences across the planting times (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e values indicated an increasing trend from very early (26.5 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) to early (30.7 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) planting and a decreasing trend with further delay in planting. The lowest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e (13.3 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was recorded for the very late planting followed by the late planting (21.9 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eT\u003c/em\u003e \u003csub\u003eYU\u003c/sub\u003e ranged from 2.2 to 3.9 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e across the planting dates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e values indicated a declining trend from very early (2.5 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) to early (2.2 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) planting and an increasing trend with a delay in planting. \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e was highest (3.9 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for the late planting followed by very late planting (3.2 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eT\u003c/em\u003e \u003csub\u003eYT\u003c/sub\u003e indicated a wide range (16.5\u0026ndash;32.9 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) across the planting dates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The values for \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e indicated an increasing trend from very early (29.0 9 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) to early (32.9 9 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) planting and a declining trend with a delay in planting. \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e was lowest (16.5 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for the very late planting followed by the late planting (25.8 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEffect of genotype on plant phenology and vegetative growth\u003c/h2\u003e \u003cp\u003eAnalysis of variance revealed presence of significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) genetic variability for all the traits determining crop phenology and crop vegetative growth (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eE\u003c/em\u003e \u003csub\u003eD\u003c/sub\u003e ranged from 10.5 to 24.5 days across the genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Earliest emergence (10.5 days) was noted for genotype El Be\u0026iuml;da followed by Elodie (10.6 days). Delayed \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e was observed for genotype Constance (24.5 days) followed by genotype D\u0026eacute;sir\u0026eacute;e (23.8 days). The remaining genotypes recorded intermediate days to plant emergence. All the genotypes were statistically different from each other.\u003c/p\u003e \u003cp\u003eEmergence percentage (\u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e) ranged from 83.1 to 100% among the eleven potato genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e was highest (100%) for genotypes Elodie and El B\u0026iuml;eda and both were statistically akin. \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e was lowest (83.1%) for genotype D\u0026eacute;sir\u0026eacute;e. 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%).\u003c/p\u003e \u003cp\u003eDays to plant maturity (\u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e) ranged from 113.9 to 139.2 across the genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The genotype Elodie had minimum \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e (113.9 days) followed by statistically different \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e (117.2 days) noted for genotype El Be\u0026iuml;da. In contrast, genotypes D\u0026eacute;sir\u0026eacute;e and Fado had the maximum and statistically different values of \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e (139.2 and 138.4 days, respectively). The remaining genotypes showed intermediate results for \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e and were placed in ascending order as: Red Sun\u0026thinsp;\u0026lt;\u0026thinsp;Rock\u0026thinsp;\u0026lt;\u0026thinsp;Red Valentine\u0026thinsp;\u0026lt;\u0026thinsp;Arsenal\u0026thinsp;\u0026lt;\u0026thinsp;Arizona\u0026thinsp;\u0026lt;\u0026thinsp;Constance\u0026thinsp;\u0026lt;\u0026thinsp;Sarpo Mira.\u003c/p\u003e \u003cp\u003e \u003cem\u003eC\u003c/em\u003e \u003csub\u003eG\u003c/sub\u003e ranged between 41.1 and 82.5% among the genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The maximum \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e (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 \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e (41.1 and 53.2%) was noted for the statistically different genotypes D\u0026eacute;sir\u0026eacute;e and Elodie, respectively. Intermediate values of \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e 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 Be\u0026iuml;da (55.0%), and Arsenal (53.8%).\u003c/p\u003e \u003cp\u003eGenotypic variation indicated wide ranges (35.1\u0026ndash;62.3 cm) in \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e 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 Be\u0026iuml;da, Rock, Arsenal, D\u0026eacute;sir\u0026eacute;e, Arizona, Red Sun, and Red Valentine) had intermediate values of \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (41.94\u0026ndash;60.61 cm).\u003c/p\u003e \u003cp\u003e \u003cem\u003eMS\u003c/em\u003e \u003csub\u003eN\u003c/sub\u003e ranged from 20.8 to 4.5 among the genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Genotypes Red Valentine and Constance exceeded the rest of genotypes with maximum and statistically different values of \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (4.5 and 4.3, respectively). The lowest \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e was noted for genotypes Sarpo Mira (2.8) and Fado (3.0). However, these genotypes were statistically unique from each other. Almost intermediate \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (3.5\u0026ndash;4.1) was noticed in the remaining genotypes (Elodie, El Be\u0026iuml;da, Arizona. Arsenal, D\u0026eacute;sir\u0026eacute;e, Red Sun, and Rock).\u003c/p\u003e \u003cp\u003eGenotypic differences were high for \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e and \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e with values ranging between 36.1\u0026ndash;51.9 and 2766\u0026ndash;5837 cm\u003csup\u003e2\u003c/sup\u003e, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The maximum values of \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (51.9) and \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (5837 cm\u003csup\u003e2\u003c/sup\u003e) were recorded for genotype Arizona. The genotype D\u0026eacute;sir\u0026eacute;e had the least values of \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (36.1) and \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (2766 cm\u003csup\u003e2\u003c/sup\u003e). These two extremes were interceded by genotypes (Elodie, El Be\u0026iuml;da, Arsenal, Rock, Sarpo Mira, Red Valentine, Fado, Red Sun, and Constance).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEffect of genotype on cumulative PAR intercepted and thermal days\u003c/h2\u003e \u003cp\u003eThere was a significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) genotypic variation for \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e ranged between 382.5 and 962.8 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The maximum \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e (962.8 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) was noted for genotype Arizona followed by statistically different genotype Fado (729.0 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). The lowest \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e (382.5 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) was exhibited by genotype D\u0026eacute;sir\u0026eacute;e. The rest of the genotypes recorded intermediate values of \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and were ranked in following ascending order: El Be\u0026iuml;da\u0026thinsp;\u0026lt;\u0026thinsp;Elodie\u0026thinsp;\u0026lt;\u0026thinsp;Rock\u0026thinsp;\u0026lt;\u0026thinsp;Arsenal\u0026thinsp;\u0026lt;\u0026thinsp;Red Valentine\u0026thinsp;\u0026lt;\u0026thinsp;Constance\u0026thinsp;\u0026lt;\u0026thinsp;Sarpo Mira.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e ranged from 42.6 to 49.5 \u003cem\u003etd\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The genotype Arizona attained maximum \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (49.5 \u003cem\u003etd\u003c/em\u003e) among the eleven genotypes. It was followed by statistically different genotype Fado with 48.1 \u003cem\u003etd TD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e. In contrast, genotype Elodie recorded the lowest value of \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (42.6 \u003cem\u003etd\u003c/em\u003e). The remaining genotypes followed an ascending trend as: Constance\u0026thinsp;\u0026lt;\u0026thinsp;El Be\u0026iuml;da\u0026thinsp;\u0026lt;\u0026thinsp;D\u0026eacute;sir\u0026eacute;e\u0026thinsp;\u0026lt;\u0026thinsp;Rock\u0026thinsp;\u0026lt;\u0026thinsp;Arsenal\u0026thinsp;\u0026lt;\u0026thinsp;Red Valentine\u0026thinsp;\u0026lt;\u0026thinsp;Red Sun\u0026thinsp;\u0026lt;\u0026thinsp;Sarpo Mira.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEffect of genotype on tuber yield and yield components\u003c/h2\u003e \u003cp\u003eResults indicated presence of significant genetic variability (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) for all the traits determining tuber yield and yield components (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eT\u003c/em\u003e \u003csub\u003eN\u003c/sub\u003e ranged from 7.6 to13.3 among the genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Highest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (13.3) was noted for the genotype Fado, and it was accompanied by statistically different genotype Constance (12.6). Furthermore, the lowest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (7.6) was noted for genotype D\u0026eacute;sir\u0026eacute;e. \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e interceded among the other genotypes.\u003c/p\u003e \u003cp\u003e \u003cem\u003eT\u003c/em\u003e \u003csub\u003eWM\u003c/sub\u003e extended from 67.6 to106.5 g among the genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The maximum values of \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e were observed in statistically similar genotypes: Arizona (106.5 g), El Be\u0026iuml;da (105.7 g) and Elodie (103.3 g). The lowest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (67.6 g) was found for genotype Fado while the remaining genotypes had intermediary values of \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (72.5\u0026ndash;95.8 g).\u003c/p\u003e \u003cp\u003eThe assessment of genotypic divergence disclosed broad fluctuations in \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e ranging from 18.4 to 29.2 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Genotype Arizona excelled among the genotypes with the highest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e (29.2 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and pursued by statistically dissimilar genotype El Be\u0026iuml;da (26.3 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The smallest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e was recorded for genotype D\u0026eacute;sir\u0026eacute;e (18.4 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). An intermediary \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e was noted for the remaining genotypes including Fado (20.6 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Constance (20.7 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Arsenal (21.7 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Red Valentine (22.5 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Rock (23.0 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Red Sun (23.3 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Sarpo Mira (23.6 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and Elodie (24.9 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e ranged between 1.2 and 5.2 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The genotypes Arizona and El Be\u0026iuml;da produced the smallest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e (1.2 and 1.3 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively). \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e was highest (5.2 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for genotype Fado followed by the statistically different genotype Constance with a 5.0 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e. The values of \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e interceded throughout the remaining genotypes.\u003c/p\u003e \u003cp\u003eAssessing the impact of genotype on \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e showed broad fluctuations in total tube yield ranging from 20.1 to 30.4 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The highest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e was found for genotype Arizona (30.4 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) followed by the statistically similar genotypes Red Sun (27.7 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Elodie (27.6 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and El Be\u0026iuml;da (27.5 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The smallest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e was recorded for genotype D\u0026eacute;sir\u0026eacute;e (18.4 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) followed by statistically similar genotypes Arsenal (24.5 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and Red Valentine (24.6 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Intermediate values of \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e were found for the remainder of the genotypes (Constance, Fado, Sarpo Mira, and Rock).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eInteractive response of planting date and genotype on plant phenology and vegetative growth\u003c/h2\u003e \u003cp\u003eThe interactive effects of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e were highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) on the traits controlling plant phenology and vegetative growth of potato (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e ranged from 9.5‒34.4 days due to the G\u0026times;E interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Emergence was accelerated with a delay in planting among all the genotypes. Genotypes Elodie, El Be\u0026iuml;da, and Red Sun had a small \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e (9.5 days) for very late planting, while genotype D\u0026eacute;sir\u0026eacute;e had the highest \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e (34.4 days) for the very early planting.\u003c/p\u003e \u003cp\u003e \u003cem\u003eE\u003c/em\u003e \u003csub\u003eP\u003c/sub\u003e ranged from 48.8 to 100% due to \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Most of the genotypes exhibited the highest \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e for the later planting dates (i.e., late to very late), while the lowest \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e was found for the earlier planting dates (i.e., very early to early). The highest and/or complete \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (100%) was found for the genotypes Elodie and El B\u0026iuml;eda across all planting dates. Among the genotypes, Constance and Fado had the lowest \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (48.8 and 51.3%, respectively) for the very early planting date.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePM\u003c/em\u003e \u003csub\u003eD\u003c/sub\u003e ranged from 97.5 to 153.5 days due to prevalence of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e 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 D\u0026eacute;sir\u0026eacute;e had the highest value for \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e (108 days) for the very late planting.\u003c/p\u003e \u003cp\u003e \u003cem\u003eC\u003c/em\u003e \u003csub\u003eG\u003c/sub\u003e ranged from 49.2 to 94.7% on account of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Plant canopy expanded most in early planting in comparison to other planting dates among all the genotypes. The highest value of \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e (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 D\u0026eacute;sir\u0026eacute;e had lower values of \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e among the genotypes across planting dates. It had the lowest \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e (28.4%) for the very late planting.\u003c/p\u003e \u003cp\u003eThe impact of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction was visible on \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e with values ranging from 23.9 to 84.0 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). A marked decline in \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e was recorded among all the genotypes with a delay in planting and vice versa. The genotype Red Valentine had the highest \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (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 \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e across the four planting dates with lowest \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (23.9 cm) for the very late planting.\u003c/p\u003e \u003cp\u003eThe response of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction revealed wide range of variation for \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (2.5\u0026ndash;4.9) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Most of the genotypes indicated a differential response to different planting dates. Among the genotypes, Red Valentine had the highest \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (4.8\u0026ndash;4.9) for the very early to late planting dates, respectively. Most of the genotypes attained lower values of \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e for the very late planting date. The genotype Sarpo Mira had lower values of \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e among the genotypes across the four planting dates. It produced the lowest value of \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (2.5) in very late planting.\u003c/p\u003e \u003cp\u003eThe impact of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction showed a marked range of variation for \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (32.0\u0026ndash;57.1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). For nearly all the genotypes, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e declined with a delay in planting. The most noteworthy \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (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 D\u0026eacute;sir\u0026eacute;e showed smaller values of \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e for all planting dates with the lowest number of leaves (32.0) for very late planting.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLA\u003c/em\u003e \u003csub\u003eP\u003c/sub\u003e was markedly affected by \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interactions with values ranging from 1966 to7289 cm\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Most of the genotypes obtained higher values of \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e for the early planting dates. \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e values declined among the genotypes for the very early and very late planting dates. The genotype Arizona had a high \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e among the genotypes throughout the planting dates. It produced maximum \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (7289 and 7039 cm\u003csup\u003e2\u003c/sup\u003e) for the early and late planting dates, respectively. Lower values of \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e were found for the genotype D\u0026eacute;sir\u0026eacute;e for all the planting dates with the smallest \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (1966 cm\u003csup\u003e2\u003c/sup\u003e) found for the very late planting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eInteractive response of planting date and genotype on cumulative PAR intercepted and thermal days\u003c/h2\u003e \u003cp\u003eThe effect of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction was highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) on \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e ranged from 109 to 1120 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e declined in all the genotypes with a delay in planting. The genotype Arizona attained high value of \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e throughout the four planting dates. It had the highest \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e (1120 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) in early planting followed by late (1057 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), very early (757 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), and very late (588 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) planting. The genotype D\u0026eacute;sir\u0026eacute;e had a low \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e for the majority of the planting dates with the lowest \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e value (108.7 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) for the very late planting dates.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTD\u003c/em\u003e \u003csub\u003eC\u003c/sub\u003e ranged between 40.6 and 69.0 \u003cem\u003etd\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Nearly all the genotypes indicated a declining trend in \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e with a delay in planting. The genotype El Be\u0026iuml;da achieved high values of \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e throughout the planting dates. It recorded the maximum \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (69.0 \u003cem\u003etd\u003c/em\u003e) in very early planting followed by early (63.9 \u003cem\u003etd\u003c/em\u003e), late (56.2 \u003cem\u003etd\u003c/em\u003e), and very late (47.1 \u003cem\u003etd\u003c/em\u003e) planting. The lowest \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e (40.6 \u003cem\u003etd\u003c/em\u003e) was exhibited by genotype D\u0026eacute;sir\u0026eacute;e in very late planting among the genotype and planting date treatment combinations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eInteractive response of planting date and genotype on tuber yield and yield components\u003c/h2\u003e \u003cp\u003eThe impact of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction was highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) on tuber yield and yield components (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There was a wide range for \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (5.3‒15.4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). All genotypes showed a decline in \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e with a delay in planting. Genotype Fado had the highest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (15.4, 13.7, 13.0, and 11.0) throughout the planting dates. The genotype D\u0026eacute;sir\u0026eacute;e had a reduced \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e for most planting dates with minimum \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (5.3) in very late planting.\u003c/p\u003e \u003cp\u003eExamination of the interaction response of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e revealed that \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e ranged from 46.9 to130.6 g (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Most of the genotypes attained high \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e values with earlier planting and indicated a declining trend with delayed planting. The maximum and statistically at par \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e was recorded for genotypes Arizona (130.6 g), Elodie (125.4 g), and El Be\u0026iuml;da (124.8 g) in early planting. Least \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (46.9 g) was noted for genotype Constance in very late planting.\u003c/p\u003e \u003cp\u003e \u003cem\u003eT\u003c/em\u003e \u003csub\u003eYM\u003c/sub\u003e ranged from 9.7 to 39.1 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e due to \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e enhanced with early planting date and declined with late and very late planting dates in all the genotypes. All the genotypes produced a greater \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e 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\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in all four planting dates (i.e., early, very early, late, and very late, respectively). Least \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e (9.7 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was noted for genotype D\u0026eacute;sir\u0026eacute;e in very late planting.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction effects revealed a wide range for \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e (0.84\u0026ndash;8.8 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) as shown by (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e values indicated an increasing trend with a delay in planting for most of the genotypes. The smallest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e (0.84 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was observed in genotype Arizona followed by genotype El Be\u0026iuml;da attaining statistically at par \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e (0.86 and 0.87 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in early and very early planting, respectively. The genotype Fado recorded the highest \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e (8.8 and 5.7 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for the late and very late planting dates, respectively.\u003c/p\u003e \u003cp\u003e \u003cem\u003eT\u003c/em\u003e \u003csub\u003eYT\u003c/sub\u003e ranged from 11.7 to 40.0 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e because of \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The values for \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e 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\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for all four planting dates i.e., early, very early, late, and very late planting, respectively. The genotypes D\u0026eacute;sir\u0026eacute;e recorded least \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e (11.7 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for the very late planting date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eInter-relationships among the growth and yield determining traits of potato\u003c/h2\u003e \u003cp\u003eThe examination of the correlation coefficients revealed statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) associations among the majority of the traits investigated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). There were positive and very strong (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.70) correlations among \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e and \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e with \u003cem\u003er\u003c/em\u003e values ranging from 0.71\u0026ndash;0.96; among \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.78\u0026ndash;0.98); between \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, and \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91\u0026ndash;0.95).\u003c/p\u003e \u003cp\u003eThe results further indicated moderately strong positive correlations (0.30\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.70) between \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e and \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33\u0026ndash;0.55); between \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e and \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30\u0026ndash;0.56); between \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e and \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41\u0026ndash;0.50); between \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e and \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0.38\u0026ndash;0.57); between \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31\u0026ndash;0.66); between \u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e and \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31\u0026ndash;0.35). The results illustrated very strong negative correlations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;\u0026minus;\u0026thinsp;0.70) between \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e and \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e = -0.73) and moderately strong negative correlations (\u003cem\u003er\u003c/em\u003e = -0.48) between \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e and \u003cem\u003eE\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eAdditional examination of the data revealed highly significant and very strong (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.70) positive correlations between \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e or \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e and most of the traits including \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e with value of \u003cem\u003er\u003c/em\u003e ranging between 0.71 to 0.87 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). As expected, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e showed a strong positive relationship with \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97). There were moderately strong negative correlations (\u0026minus;\u0026thinsp;0.40\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.70) between \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYU\u003c/sub\u003e and \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, \u003cem\u003eTW\u003c/em\u003e\u003csub\u003eM\u003c/sub\u003e, and \u003cem\u003eTY\u003c/em\u003e\u003csub\u003eM\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.34 to \u0026minus;\u0026thinsp;0.51).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of key yield determining traits in potato\u003c/h2\u003e \u003cp\u003eConsidering 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.\u003c/p\u003e \u003cp\u003eThe procedure of stepwise regression analysis is elaborated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. 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 (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;96.3, 94.7, and 94.2%, respectively) in tuber yield (Table\u0026nbsp;6). In model No. 12, all the traits except \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, in model No. 22, four traits (\u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e, and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e), while in model No. 23, only three traits (\u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e) had a significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;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).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eAssessing variations in weather conditions\u003c/h2\u003e \u003cp\u003eThis 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.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eAssessing effects of planting date\u003c/h2\u003e \u003cp\u003eAssessing 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 (\u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e), while a delay in planting (i.e., late, and very late planting date) advanced the \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e. Such trends might possibly be due to the higher maximum temperatures during early plantings (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This aligns with previous findings by MacKerron (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), Khan (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Khan et al (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) which suggest that temperatures higher than optimum (23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C) may lead to delayed tuber sprouting and, consequently, delayed plant emergence. Furthermore, a delay in planting also accelerated plant maturity (\u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e). 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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which adversely impacted foliage and accelerated maturity. Temperature exerts a significant impact on leaf formation, expansion, and senescence (Kirk and Marshall, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Vos, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Firman et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Struik and Ewing, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Van Delden et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Fleisher and Timlin, 2006; Fleischer et al., 2006; Struik, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eTemperature 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., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our results clearly indicated that planting date determines the timing of a crop's phenological stages during the growing season (Sadras and Calderini, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe extent of green canopy cover (\u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e) exhibited a positive correlation with planting date. Early planting resulted in a more substantial \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e 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 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) and Struik et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). 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\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e) may negatively affect the expansion of green canopy cover in potato (Struik, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth plant height (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e) and number of mother stems plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (\u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e) 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., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As previously mentioned, air temperatures were comparatively higher during early plantings (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e) 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., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). 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., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Temperature strongly influences stem elongation and branching (Marinus and Bodlaender, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Allen and Scott, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Struik et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1989\u003c/span\u003e, Almekinders and Struik, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1994\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe number of leaves plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (\u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e) and leaf area plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (\u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e) increased with early planting, reflecting the positive impact of higher temperatures and prolonged photoperiods (Wolf et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Almekinders and Struik, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Ewing, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Fleisher et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Similarly, early planting maximized cumulative PAR intercepted (\u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e) and thermal days (\u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e) due to an extended \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e with optimal conditions. Conversely, late planting resulted in reduced \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e due to shortened \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, indicating the importance of planting date for efficient light interception and heat accumulation.\u003c/p\u003e \u003cp\u003ePlanting date significantly influenced tuber yield and its components. Early planting resulted in higher values for mean weight of marketable tuber (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e), marketable tuber yield (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e), and total tuber yield (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e), while later planting dates had a detrimental effect on these crucial parameters. This aligns with previous research by Khan et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which highlighted the impact of planting time on tuber production. As discussed previously, a delay in planting led to early plant maturity (\u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e) or shortening of growing period (Kawakami et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); consequently, plants produced fewer tubers (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e) due to insufficient \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, resulting in a reduction in overall plant growth and tuber yield (Struik et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). On the contrary, early planting initiated delayed plant maturity causing longer growing period and enhanced vegetative growth with higher plant\u0026rsquo;s capacity to intercept photosynthetically active radiation (\u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINT\u003c/sub\u003e) as well as accumulate more thermal days thereby ensuring high tuber yield (Ojeda et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding 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., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eAssessing effects of genotype\u003c/h2\u003e \u003cp\u003eThe study revealed substantial genotypic variability for traits relevant with plant phenology and vegetative growth, \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, 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.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For instance, early maturing genotypes such as Elodie, El B\u0026iuml;eda, and Red Sun exhibited lower values of \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e and \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, while late maturing genotypes e.g., D\u0026eacute;sir\u0026eacute;e, Fado, Sarpo Mira, Constance, and Arizona gave higher values of \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e and \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e. Most of the late maturing genotypes also exhibited higher values of \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eAP\u003c/sub\u003e, \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e compared to early maturing genotypes. Kooman and Rabbinge (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) 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.\u003c/p\u003e \u003cp\u003eThe improved photosynthetic capability and accumulation of assimilates led to enhanced marketable tuber yield in certain genotypes (Rojoni et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). 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., D\u0026eacute;sir\u0026eacute;e) was 10.3 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e 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, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Previous studies by Pashiardis (1987), Spitters (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), and Van Delden et al. (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), Khan et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003eb\u003c/span\u003e) have explored these factors.\u003c/p\u003e \u003cp\u003eOur findings emphasize the significant impact of genotype on various aspects of potato growth, development, and yield (Kooman et al.,1996a; Struik and Wiersema, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; P\u0026eacute;rez et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dash et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Genotypic diversity is evident in plant phenology, vegetative growth, PAR interception, thermal requirements, and tuber yield-related traits (Tessema et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eAssessing interactive effects of planting date and genotype\u003c/h2\u003e \u003cp\u003eThe interactive effects of planting date and genotype (i.e., \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e 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.\u003c/p\u003e \u003cp\u003eDelayed planting generally accelerated \u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e across all genotypes. Genotype Elodie displayed the fastest emergence in very late planting, while genotype D\u0026eacute;sir\u0026eacute;e had the slowest emergence in very early planting. Time to maturity (\u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e) shortened with delayed planting across all genotypes. Late maturing genotypes (e.g., D\u0026eacute;sir\u0026eacute;e, Fado, and Sarpo Mira) consistently required the most time to mature, while early maturing genotypes (e.g., Elodie, El B\u0026iuml;eda, 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., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and how they interact with seasonal variations (Struik, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). 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., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Alemayehu and Jemberie, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interaction significantly influenced the \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, with early planting resulting in the most extensive canopy growth across genotypes. Late maturing genotype Arizona consistently exhibited the highest \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e 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, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Schittenhelm et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Furthermore, delayed planting led to reduced values of traits including \u003cem\u003eH\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, and \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e 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 \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e in very early planting, while genotype Sarpo Mira had the shortest plants and attained lowest \u003cem\u003eMS\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e in very late planting. Genotype Arizona consistently had the highest \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, and \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e values, while genotype D\u0026eacute;sir\u0026eacute;e consistently had lower values. This demonstrates how \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interactions can influence the physical characteristics of potato plants (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDelayed planting generally resulted in decreased \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, indicating reduced light interception and lower thermal accumulation, respectively among the genotypes. Late maturing genotypes Arizona consistently exhibited the highest accumulation of \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINT\u003c/sub\u003e and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, 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 \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINT\u003c/sub\u003e by the crop best explains differences in potato yield across environments and genotypes (Allen and Scott, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Kooman et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1996a\u003c/span\u003e; Schittenhelm et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLater plantings induced a declining trend in tuber yield and yield components among the genotypes. Genotype Arizona consistently outperformed others in terms of \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e 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.\u003c/p\u003e \u003cp\u003eResults 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.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-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., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Hassanpanah et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zakir \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eAssessing inter-relationship among traits\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003eHighly strong relationship between yield and growth attributes such as \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e, \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e, \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e suggest that these are the critical determinants of yield formation in potato (Khan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003eb\u003c/span\u003e). 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 \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and consequently, increased tuber yields (Ojeda et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Results indicated that genotypes or environmental conditions leading to higher production of marketable tubers determines the overall tuber yield.\u003c/p\u003e \u003cp\u003eThe 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 \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e and smaller \u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e (Santos et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eIn 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., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of key yield-determining traits in potato\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003eThe findings suggest that a genotype with optimum period of emergence, and high green canopy cover over longer period may contribute to higher values \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e resulting in higher tuber yields in potato crops. Several studies have indicated that canopy cover (Khan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) and \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e are important determinants of yield differences among the genotypes (Allen and Scott, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Burstall and Harris, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Van Der Zaag and Doornbos, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Kooman et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1996a\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur 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, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe 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 D\u0026eacute;sir\u0026eacute;e 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.\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbebe T, Wongchaochant S, Taychasinpitak T (2013) Evaluation of Specific gravity of potato varieties in Ethiopia as a criterion for determining processing Quality. 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Potato Research 27: 51\u0026ndash;73.\u003c/li\u003e\n \u003cli\u003eVan der Zaag DE, Doornbos JH (1987) An atempt to explain differences in the yielding ability of potato cultivars based on differences in cumulative light interception, utilization efficiency of foliage and harvest index.\u0026nbsp;Potato Research\u0026nbsp;30: 551\u0026ndash;568.\u003c/li\u003e\n \u003cli\u003eVos J (1995) Foliar development of the potato plant and modulations by environmental factors. In: Kabat P, Van den Broek BJ, Marshall B, Vos J (Eds.), Modelling and parameterization of the soil-plant-atmosphere system. A comparison of potato growth models. Wageningen Pers, Wageningen, pp. 21\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003eWang CL, Shen SH, Zhang S., Li QZ, Yao YB (2015) Adaptation of potato production to climate change by optimizing sowing date in the Loess Plateau of central Gansu, China. Journal of Integrative Agriculture 14: 398\u0026ndash;409.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWolf S, Marani A, Rudich J (1990) Effects of temperature and photoperiod on assimilate partitioning in potato plants.\u0026nbsp;Annals of Botany\u0026nbsp;66: 513\u0026ndash;520.\u003c/li\u003e\n \u003cli\u003eWu F, Guo S, Huang W, Han Y, Wang Z, Feng L, Wang G, Li X, Lei Y, Yang B, Xiong S, Zhi X, Chen J, Xin M, Wang Y, Li Y (2023) Adaptation of cotton production to climate change by sowing date optimization and precision resource management.\u0026nbsp;Industrial Crops and Products\u0026nbsp;203: 117167.\u003c/li\u003e\n \u003cli\u003eYin X, Kropff MJ, McLaren G, Visperas RM (1995) A nonlinear model for crop development as a function of temperature.\u0026nbsp;Agricultural and Forest Meteorology\u0026nbsp;77: 1\u0026ndash;16.\u003c/li\u003e\n \u003cli\u003eYin X, Struik PC, Tang J, Qi C, Liu T (2005) Model analysis of flowering phenology in recombinant inbred lines of barley.\u0026nbsp;Journal of Experimental Botany\u0026nbsp;56: 959\u0026ndash;965.\u003c/li\u003e\n \u003cli\u003eZakir M (2018). Review on genotype \u0026times; environment interaction in plant breeding and agronomic stability of crops.\u0026nbsp;Journal of Biology, Agriculture and Healthcare\u0026nbsp;8: 14\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eZhang K, Wang RY, Li QZ, Zhao H, Wang HL, Guo L, Zhang XY (2012) Effects of sowing date on the growth and tuber yield of potato in semi-arid area of loess plateau in central Gansu Province of Northwest China. Chinese Journal of Ecology 31: 2261\u0026ndash;2268.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"potato-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"potr","sideBox":"Learn more about [Potato Research](http://link.springer.com/journal/11540)","snPcode":"11540","submissionUrl":"https://www.editorialmanager.com/potr/default2.aspx","title":"Potato Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adaptation, crop management, planting time, plant phenology, seasonal variation, yield","lastPublishedDoi":"10.21203/rs.3.rs-4720912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4720912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePotato tuber yield-determining seasonal changes, especially in subtropical growing settings, are poorly investigated. This study examined eleven potato genotypes \u0026lsquo;\u003cem\u003eG\u003c/em\u003e\u0026rsquo; under four planting dates \u0026lsquo;\u003cem\u003eP\u003c/em\u003e\u0026rsquo; (very early (02 Oct), early (14 Oct), late (26 Oct), and very late (07 Nov)) and their interactive response (\u003cem\u003eP\u003c/em\u003e\u0026times;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 (\u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e) and maximizing green canopy cover (\u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, 72.3%), mother stems plant\u003csup\u003e-1\u003c/sup\u003e (\u003cem\u003eM\u003c/em\u003e\u003csub\u003eSN\u003c/sub\u003e, 4.1), leaf number plant\u003csup\u003e-1\u003c/sup\u003e (\u003cem\u003eL\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, 50.8), leaf area plant\u003csup\u003e-1\u003c/sup\u003e (\u003cem\u003eLA\u003c/em\u003e\u003csub\u003eP\u003c/sub\u003e, 5343 cm\u003csup\u003e2\u003c/sup\u003e), cumulative photosynthetic active radiation (\u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e, 900.9 MJ m\u003csup\u003e-2\u003c/sup\u003e), thermal days (\u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, 52.9 \u003cem\u003etd\u003c/em\u003e), and tubers plant\u003csup\u003e-1\u003c/sup\u003e (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, 11.8), marketable tuber weight (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eWM\u003c/sub\u003e, 103.0 g), marketable (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e, 30.7 t ha\u003csup\u003e-1\u003c/sup\u003e), and total (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e, 32.9 t ha\u003csup\u003e-1\u003c/sup\u003e) tuber yield. Late plantings reduced \u003cem\u003ePM\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e 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 \u003cem\u003eP\u003c/em\u003e\u0026times;\u003cem\u003eG\u003c/em\u003e interactions. The genotype \u0026lsquo;Arizona\u0026rsquo; outperformed others with maximum \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYM\u003c/sub\u003e (29.2 t ha\u003csup\u003e-1\u003c/sup\u003e) and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eYT\u003c/sub\u003e (30.4 t ha\u003csup\u003e-1\u003c/sup\u003e) across planting dates. We identified key traits including days to emergence (\u003cem\u003eE\u003c/em\u003e\u003csub\u003eD\u003c/sub\u003e), \u003cem\u003eC\u003c/em\u003e\u003csub\u003eG\u003c/sub\u003e, \u003cem\u003ePAR\u003c/em\u003e\u003csub\u003eINTC\u003c/sub\u003e, and \u003cem\u003eTD\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e, 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.\u003c/p\u003e","manuscriptTitle":"Effects of Planting Date and Genotypes on Potato Growth and Yield Determination in a Sub-Tropical Continental Growing Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 10:11:41","doi":"10.21203/rs.3.rs-4720912/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2024-10-01T02:31:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-18T21:22:10+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-11T05:28:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-11T03:33:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Potato Research","date":"2024-07-10T20:06:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"potato-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"potr","sideBox":"Learn more about [Potato Research](http://link.springer.com/journal/11540)","snPcode":"11540","submissionUrl":"https://www.editorialmanager.com/potr/default2.aspx","title":"Potato Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2b6901fd-f7e1-458f-a0ab-bf002938e755","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:06:56+00:00","versionOfRecord":{"articleIdentity":"rs-4720912","link":"https://doi.org/10.1007/s11540-024-09833-x","journal":{"identity":"potato-research","isVorOnly":false,"title":"Potato Research"},"publishedOn":"2024-12-18 15:56:57","publishedOnDateReadable":"December 18th, 2024"},"versionCreatedAt":"2024-08-05 10:11:41","video":"","vorDoi":"10.1007/s11540-024-09833-x","vorDoiUrl":"https://doi.org/10.1007/s11540-024-09833-x","workflowStages":[]},"version":"v1","identity":"rs-4720912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4720912","identity":"rs-4720912","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}