Evaluation of Yield Potential and Quality of Daikon (Raphanus sativus L. convar. acanthiformis Sazon.) Cultivars Under Different Sowing Dates

Abstract

Climate variability necessitates the optimization of sowing dates for vegetable crops to stabilize yields and mitigate abiotic stress risks. This study aimed to evaluate the effect of sowing dates on the productivity of daikon radish (Raphanus sativus L. convar. acanthiformis Sazon.) cultivars Gulliver and Minowase under medium-podzolic, light loamy soil conditions with a pH (pHKCl) of 6.74 during the period 2022–2024. Field experiments were conducted across four sowing dates (ranging from July to early August), accounting for the hydrothermal conditions of the growing season. Effective air temperatures ranged from 428 to 950 °C, with precipitation levels between 36.9 and 252.3 mm. It was established that the sowing date significantly influenced daikon yield (p < 0.001). A significant positive correlation was identified between yield and precipitation (r = 0.76–0.84; p < 0.05), whereas the correlation between yield and the sum of effective temperatures was weak to moderate and predominantly negative (r = −0.62 to −0.10). The highest yields were achieved with sowing in the third ten-day period of July: 54.6 t ha⁻¹ for the Gulliver cultivar and 58.9 t ha⁻¹ for the Minowase cultivar. The Minowase cultivar consistently outperformed Gulliver in terms of yield and exhibited higher ecological plasticity under fluctuating hydrothermal conditions. These findings confirm the feasibility of optimizing sowing dates as an effective adaptive tool for enhancing the stability of daikon production amidst climate change.

1. Introduction

Global food and energy crises, alongside climate change, necessitate sustainable land use and crop adaptation [1,2]. With the population projected to exceed 10 billion by 2050, food security faces threats from demographic pressure and disruptions like wars or pandemics [3,4,5,6]. In this context, vegetable growing is vital for economic stability and public health [7,8,9]. However, insufficient consumption currently contributes to over 2.6 million annual deaths due to chronic diseases [10,11].

Determining the optimal sowing date is critical for yield forecasting and maximizing resource efficiency [12,13,14]. Incorrect timing can nullify the benefits of fertilizers and irrigation [15]. Optimizing sowing dates acts as an adaptation strategy to mitigate heat stress [16], regulate pest populations [17], and ensure optimal light for photosynthesis [18]. This approach minimizes biomass loss from water stress [19,20] and improves biochemical quality [21,22]. Furthermore, daikon serves as an effective intermediate crop for sustainable soil management [23].

Daikon (Raphanus sativus L. convar. acanthiformis Sazon.) is a widely cultivated cool-season crop [24,25,26] valued for the nutritional and medicinal properties of its roots, leaves, and pods [27,28,29,30,31]. However, environmental constraints strongly influence its quality. High temperatures and long days induce premature bolting and pungency [32], while early sowing (e.g., July) increases root pithiness compared to later dates [33]. Lower temperatures (4 °C) favor root biomass accumulation, whereas higher temperatures (18/14 °C) shift development toward leaf formation [26]. Consequently, sowing times must be adjusted regionally [34] to avoid defects caused by moisture deficits [33].

In the Ukrainian Forest Steppe, a temperature rise of 0.7–0.9 °C [35] is shifting climatic zones northward [36]. While this extends the vegetation period, it intensifies moisture deficits and negatively affects productivity [37,38]. These changes require immediate adaptation measures [39], including the selection of heat-tolerant varieties [40] and the alignment of harvest times with market demand [41].

In modern agronomy, the sowing date acts as a complex indicator of the photothermal conditions of ontogenesis. Understanding the physiological response of daikon to day length and temperature is critical for ensuring stable production amidst climate change.

The aim of this research was to determine the impact of the sowing date on daikon yield, identify the governing climatic factors, and establish the optimal sowing window to ensure stable, high-quality productivity. Additionally, the study provides a comparative analysis of the yield performance between the Gulliver and Minowase varieties.

2. Materials and Methods

2.1. Site Description, Climate, and Soil Characteristics

The experimental study was carried out between 2022 and 2024 at the National University of Life and Environmental Sciences of Ukraine (NULES), in cooperation with the Ulbroka Research Centre of the Latvia University of Life Sciences and Technologies (LBTU). The field experiment was carried out at experimental sites with approximate geographic coordinates of 50°22′42.21″ N, 30°30′05.17″ E, at an altitude of 158 m above sea level.

The soil of the experimental plot is characterized as dark gray, medium-podzolic, light loamy, with a soil pH (pHKCl) of 6.74 (

Table 1. Physicochemical properties and nutrient status of the soil.

Parameter (Units)	(1) Test Results	(2) Expanded Uncertainty	(3) Fertility Class Range	(3) Interpretation
Soil pH (pHKCl)	6.74	0.02	6.1–7.0	Neutral
Soil Organic Matter (Humus), %	2.71	0.17	2.1–3.0	Medium
Nitrate Nitrogen (NO3), mg kg−1	6.42	0.10	16–24	Medium
Ammonium Nitrogen (NH4), mg kg−1	11.72	0.12
Available Phosphorus (P2O5), mg kg−1	568.83	1.89	>200	Very high
Available Potassium (K2O), mg kg−1	152.93	3.10	121–180	High

Note: ⁽¹⁾ Results are reported on an air-dry basis; ⁽²⁾ Expanded uncertainty was calculated by multiplying the reproducibility standard deviation (determined via the modified Horwitz-Thompson equation) by a coverage factor of k = 2 (95% confidence level); ⁽³⁾ Soil fertility indicators are based on DSTU 4362:2004 (Soil Quality: Soil Fertility Indicators) [42].

), which corresponds to a neutral reaction. The humus horizon is 24–28 cm. The experimental plot is characterized by a medium humus (soil organic matter) content of 2.71%. According to the test results, nutrient concentrations are as follows: nitrate nitrogen (NO₃) at 6.42 mg/kg and ammonium nitrogen (NH₄) at 11.72 mg kg⁻¹ (medium fertility class); available phosphorus (P₂O₅) content is 568.83 mg kg⁻¹, which is a very high level; and available potassium (K₂O) content is 152.93 mg kg⁻¹, which corresponds to a high level. The soil is characterized by a medium sum of exchangeable bases, ranging from 6.8 to 8.6 meq 100 g⁻¹. The physical clay content is 20–21%, and the bulk density is 1.36–1.38 g cm⁻³. The groundwater table depth exceeds 7 m.

Soil samples were analyzed for their physicochemical and agrochemical properties using standardized methods, in accordance with national (DSTU) and international (ISO) protocols [43,44,45,46]:

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Soil pH (Exchangeable Acidity): Potentiometric determination was performed in a 1 M KCl suspension with a soil-to-solution ratio of 1:5, following ISO 10390:2021 (adopted as DSTU ISO 10390:2022). Measurements were conducted using a calibrated glass electrode pH meter.

-

Mineral Nitrogen (${\mathrm{N}\mathrm{O}}_3^{-}$ and ${\mathrm{N}\mathrm{H}}_4^{+}$): The content of nitrate and ammonium nitrogen was determined according to DSTU 4729:2007 (modified by the NSC ISSAR). Nitrates were measured using the ion-selective electrode method, while ammonium nitrogen was determined colorimetrically using Nessler’s reagent.

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Available Phosphorus (P₂O₅) and Exchangeable Potassium (K₂O): These macronutrients were extracted using the Chirikov modified method (DSTU 4115-2002) with 0.5 M acetic acid (CH₃COOH) at a 1:5 soil-to-solution ratio. Phosphorus concentration was quantified spectrophotometrically via the molybdenum blue complex method, and potassium was determined using flame photometry.

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Soil Organic Matter (SOM): The total organic carbon (humus) content was determined by the Tyurin method as specified in DSTU 4289:2004 (clause 8.1). This procedure involves the wet oxidation of organic matter with a potassium dichromate (K₂Cr₂O₇) and sulfuric acid (H₂SO₄) solution, followed by photometric measurement of Cr³⁺ concentration.

During the experimental period (July–October) in 2022–2024, the thermal regime consistently exceeded long-term averages by 0.9–2.6 °C, with the maximum temperatures recorded in August (22.3–23.7 °C) and an unstable temperature distribution in September (12.7–20.5 °C) (Figure 1). Precipitation during the active vegetation of daikon was unevenly distributed: while July and August were characterized by moderate moisture, the autumn months (September–October) saw a range of 50.2 to 81.5 mm, with October specifically exceeding historical norms by 35.5 mm. Such excessive soil moisture during these final stages of ontogenesis induced soil hypoxia, restricting root respiration and dry matter accumulation. The resulting waterlogging triggered root cracking, increased the risk of pathogenesis, and impaired the storage life of daikon due to intensive metabolic degradation of tissues. Furthermore, these conditions at the late stages of vegetation complicated harvesting and diminished the overall marketable quality of the yield.

2.2. Plant Material and Experimental Factors

In Ukraine’s commercial daikon cultivation, only a limited number of varieties are widely grown, with Gulliver and Minowase being the most prevalent. These specific varieties were selected for the research as they belong to different maturity groups: Gulliver is early-maturing (35–50 days), while Minowase is mid-maturing (60–70 days). The scientific focus was on a comparative evaluation of varieties with different genetic and geographical origins—the domestic Gulliver (Nasko) and the foreign-bred Minowase (Satimex)—which are characterized by distinct adaptive properties and economic traits.

The experiment was designed as a two-factor trial. Factor A (variety) included Gulliver and Minowase. The Gulliver variety was designated as the control because it is a well-established domestic cultivar recognized for its stable yield across Ukraine. Factor B (sowing date) consisted of four levels: the first, second, and third ten-day periods of July, and the first ten-day period of August (Figure 2). The second ten-day period of July was designated as the control, as it represents the traditionally recommended agro-climatic window for daikon cultivation in the Forest Steppe zone of Ukraine. This selection allowed for a comparative analysis of how shifting sowing dates toward later or earlier periods affects crop performance and the specific adaptive responses of the studied varieties in the context of contemporary climate trends.

Since daikon is a long-day crop, it is crucial to establish a sowing date that ensures active growth occurs during periods of shortening daylight. This prevents premature flowering (bolting). Furthermore, selecting the correct date helps avoid the peak emergence period of the cabbage root fly (Delia floralis Fal.), thereby minimizing plant damage. Temperature reduction and increased air and soil moisture are also critical, as these factors prevent the development of bitterness and pithiness in the root crops. At the same time, sowing dates, depending on the length of the growing season, determine the harvest timing; it is essential that the roots reach marketable size and remain suitable for storage without overmaturing.

The research was conducted in three-fold spatial replication (on-site) with randomization and temporal repetition (during the years 2022, 2023, 2024). The registered area of the plot is 5 m². The seeds were sown by hand, the plant placement pattern 45 × 10 cm. The seeding depth was 4–6 cm.

2.3. Experimental Design and Field Management

The experiments were conducted during 2022–2024 on different plots within the same crop rotation to maintain methodological consistency and avoid the effects of soil fatigue. Cucumber served as the preceding crop, followed by daikon cultivated annually as a catch crop. Pre-sowing soil preparation included the removal of crop residues and rotary tilling to a depth of 20–22 cm. This tillage method ensured a fine-aggregated structure of the root zone, which is a decisive factor for the unimpeded elongation and proper morphological development of daikon roots. Crop management involved regular inter-row cultivation. Pest control (specifically against the cabbage fly, Delia radicum) was conducted using the biological insecticide Aktofit (active ingredient—aversectin C, 0.2%).

2.4. Sampling and Morphometric Measurements

The studied daikon cultivars belong to different maturity groups: the early-maturing Gulliver and the mid-maturing Minowase, which necessitated a differentiated approach to determining their technical maturity at 50 and 60 days after emergence, respectively. Harvesting was carried out at the BBCH 49 stage, when the roots reached their typical cultivar-specific form and size [47]. Harvesting within these timeframes is optimal: earlier harvesting leads to yield loss due to undersized roots (mall-sized fraction), while later harvesting results in a decline in commercial quality caused by over-ripening, pithiness (cavity formation), and an increased risk of disease infection.

The pre-harvest phase involved visual monitoring of the experimental plots, followed by the determination of plant density. The boundaries of each plot were clearly demarcated. Harvesting was performed using the total harvest method, conducted separately for each experimental unit. To minimize experimental errors, harvesting across the test plots and buffer strips was carried out sequentially by replication. All field operations were completed within 1–2 days to ensure environmental homogeneity. The yield structure was analyzed by fractionating the produce into marketable and non-marketable components, with further classification into standard and non-standard products. The weight of each fraction was determined using the strain-gauge (tensometric) weighing method. Particular attention was paid to the unification of sample preparation, specifically by ensuring uniform foliage trimming of the root crops across all experimental treatments. The average weight of root crops was determined in 10 kg samples for each replication separately. The quantity of the root crops was counted and the average mass of the marketable root crops was determined with an accuracy of one gram. In each replication of the variant there was made a complete analysis of the unmarketable root crops. The unmarketable roots were sorted into those affected by diseases, those damaged by pests, cracked, blooming, undersized (the root crops that had not reach he marketable size in diameter of 3 cm), and the deformed root crops.

2.5. Biochemical Analysis

The biochemical analyses were conducted in the Laboratory of the Ukrainian Institute of Plant Varieties, using generally accepted methods, namely, the dry matter in accordance with DSTU 7804:2015; the total sugar—DSTU 4954:2008; ascorbic acid—DSTU ISO 6557-2:2014 [48,49,50]:

1. Determination of Dry Matter Content. The dry matter content was determined by drying the samples to a constant weight. From a representative homogenized sample of vegetables, at least two subsamples of 25–50 g each were weighed with an accuracy of 0.01 g into pre-weighed porcelain or Petri dishes. The dishes were then placed in a drying oven preheated to 120 °C. Upon loading, the temperature typically decreased, and the samples were maintained at 100–102 °C for 20–30 min to inactivate enzymatic processes.

Subsequently, the material was dried at 60–70 °C until it reached an air-dry state (indicated by the characteristic brittleness of the material). Final drying was conducted at 100 °C for 4 h. After cooling in a desiccator, the dishes were weighed and re-dried for 1 h at the same temperature. This procedure was repeated until a constant mass was achieved, with a maximum permissible difference between two weighings of 0.01–0.02 g.

2. Determination of Ascorbic Acid (Vitamin C). The method was based on the reducing properties of ascorbic acid. The indicator 2,6-dichlorophenolindophenol (blue dye) was reduced to a colorless compound by plant extracts containing vitamin C. Acidic plant extracts were titrated with a standard solution of the dye until a faint pink coloration appeared, resulting from an excess of the indicator in an acidic medium.

3. Quantitative Determination of Sugars. The quantitative determination of sugars was based on the ability of reducing sugars (containing a free aldehyde or ketone group) to reduce copper (II) sulfate to copper (I) oxide in an alkaline medium. The copper (I) oxide precipitate was then dissolved in iron (III) sulfate in the presence of sulfuric acid. During this reaction, the copper (I) oxide was oxidized by iron, reducing it to Fe (II), which was subsequently titrated with a potassium permanganate solution. The disaccharide content was determined using the same procedure following preliminary hydrolysis with dilute mineral acids.

Note: Since the sugar concentration in the solution is not directly proportional to the mass of the copper (I) oxide precipitate (due to the partial decomposition of monosaccharides and Fehling’s solution during heating), calculations were performed using Bertrand’s tables. These tables provide an empirically established relationship between the precipitate mass and the glucose content. Strict adherence to the standardized conditions (temperature, heating duration, and solution concentrations) was maintained to ensure the accuracy of the results.

2.6. Organoleptic (Sensory) Evaluation

The taste of the varieties was assessed by tasting fresh root crops immediately after their harvesting. For tasting there were used typical root vegetables of this variety, healthy and normally developed. They were cut across and each taster was given one segment of each root crop vegetable. The overall tasting score was given in points as a summary assessment of the attractiveness of the appearance, consistency, juiciness and taste of the root vegetable pulp. The overall tasting score was given in points as a summary assessment of the attractiveness of the appearance, consistency, juiciness and taste of the root vegetable pulp. The taste qualities were determined by a nine-point scale: 1—very tasteless; 3—tasteless; 5—average; 7—tasty; 9—very tasty. The consistency of the pulp was also determined: very tender, tender, rough; juiciness: very juicy, juicy, not juicy.

2.7. Statistical Analysis

Statistical analysis of the research data was performed using Statistica 13.1 (StatSoft, Inc., Tulsa, OK, USA) and JASP (Version 0.95.4, JASP Team, 2025). The normality of the data distribution was verified through a visual analysis of Normal Probability Plots of Residuals and the Shapiro–Wilk test. Since the model residuals followed a normal distribution, a three-way Analysis of Variance (Factorial ANOVA) was applied to determine the influence of the studied factors. Data visualization and the construction of charts were carried out using Microsoft Excel 2024 (Microsoft Corp., Redmond, WA, USA).

To identify specific differences between the daikon varieties, Tukey’s Honestly Significant Difference (HSD) post hoc test was employed. Differences were considered statistically significant at p < 0.05 (incorporating the Bonferroni correction where applicable). The direction and strength of the relationships between the studied parameters were established by calculating the Pearson correlation coefficient [51].

3. Results

The Normal Probability Plot (P-Plot) confirms the normal distribution of yield residuals across the studied factors: year, variety, and sowing date (Figure 3). Since most experimental points (blue circles) are aligned along the diagonal reference line without significant deviations, this indicates statistical homogeneity of the data and justifies the use of ANOVA for evaluating the research results.

The ANOVA results indicate that the dominant factor influencing yield is the sowing date (F = 134.4). In combination with variety (F = 99.4) and year (F = 13.4), it determines the formation of crop productivity, with all mentioned factors showing high statistical significance (p < 0.001) (

Table 2. Results of a three-way ANOVA of the effects of year, variety, and sowing date on yield.

Effect	ss	Degr. of Freedom	MS	F	p
Year	69	2	34.5	13.4	0.000 *
Variety	257	1	256.5	99.4	0.000 *
Sowing dates	1041	3	347	134.4	0.000 *
Year × Variety	1	2	0.7	0.3	0.75
Year × Sowing dates	124	6	20.7	8	0.000 *
Variety × Sowing dates	30	3	10	3.9	0.015 *
Year × Variety × Sowing dates	18	6	3	1.2	0.347

Note: *—significant at the 0.05 level.

). It is worth noting the significant interaction between the “Year × Sowing date” factors, which suggests the absence of a universal calendar date for sowing and emphasizes the need for its adaptation to the hydrothermal conditions of a specific year. In contrast, the non-significant interaction between year and variety (p = 0.75) confirms the stability of the variety’s genetic potential regardless of the growing season conditions.

The box plot analysis shows that the yield of the daikon varieties at different sowing dates does not show any outliers but fails the normality test because the mean and median are at different points of distribution (Figure 4).

During the 2022–2024 period of sowing and harvesting of daikon, depending on the variety and sowing time, the sum of effective air temperatures (over 10 °C) fluctuated between 428 and 950 °C, and the sum of precipitation ranged from 36.9 to 252.3 mm. The results of the correlation analysis indicate that precipitation levels are the primary factor influencing the yield of both daikon radish cultivars (

Table 3. Correlation analysis of the relationship between the daikon yield and the agrometeorological indicators (the sum of air temperatures and total precipitation) in 2022–2024.

Factor A, Variety	Factor B, Sowing Date	Variable	n	Pearson’s r	p	Covariance
Gulliver	First ten-day period of July	Yield, t ha−1—Sum of temperatures above 10 °C, °C	9	−0.598	0.089	−329.2
		Yield, t ha−1—Precipitation amount, mm	9	0.803 **	0.009	148.8
	Second ten-day period of July	Yield, t ha−1—Sum of temperatures above 10 °C, °C	9	−0.103	0.792	−60.02
		Yield, t ha−1—Precipitation amount, mm	9	0.839 **	0.005	16.73
	Third ten-day period of July	Yield, t ha−1—Sum of temperatures above 10 °C, °C	9	0.035	0.928	19.40
		Yield, t ha−1—Precipitation amount, mm	9	0.561	0.116	72.30
	First ten-day period of August	Yield, t ha−1—Sum of temperatures above 10 °C, °C	9	0.035	0.929	16.51
		Yield, t ha−1—Precipitation amount, mm	9	0.588	0.096	70.64
Minowase	First ten-day period of July	Yield, t ha−1—Sum of temperatures above 10 °C, °C	9	−0.620	0.075	−354.92
		Yield, t ha−1—Precipitation amount, mm	9	0.624	0.073	76.71
	Second ten-day period of July	Yield, t ha−1—Sum of temperatures above 10 °C, °C	9	−0.627	0.071	−367.90
		Yield, t ha−1—Precipitation amount, mm	9	0.648	0.059	38.25
	Third ten-day period of July	Yield, t ha−1—Sum of temperatures above 10 °C, °C	9	−0.094	0.810	−59.27
		Yield, t ha−1—Precipitation amount, mm	9	0.602	0.086	90.09
	First ten-day period of August	Yield, t ha−1—Sum of temperatures above 10 °C, °C	9	0.272	0.480	137.99
		Yield, t ha−1—Precipitation amount, mm	9	0.759 *	0.018	62.76

Note: * p < 0.05, ** p < 0.01.

). This is confirmed by a statistically significant positive correlation, which is particularly pronounced for the Gulliver cultivar during July sowings (r = 0.803–0.839, p < 0.01) and for the Minowase cultivar during early August sowings (r = 0.759, p < 0.05). The study further revealed that the impact of cumulative temperatures above 10 °C on daikon yield depends on both the cultivar and the sowing period. For the Gulliver cultivar, the negative impact of temperature diminishes when sowing is shifted from early July (r = −0.598) to August, when the correlation becomes neutral. The Minowase cultivar exhibits higher thermal sensitivity during July sowings, demonstrating a moderate negative correlation (r = −0.620–−0.627), which is neutralized when transitioning to August sowing (r = 0.272). Although the temperature factor is not a critical limiting factor (p > 0.05), later sowing dates are more favorable for both cultivars due to the reduction in heat stress on the plants.

Regression analysis established a quantitative relationship between yield and hydrothermal conditions: an increase in the cumulative effective air temperatures by every 10 °C resulted in a yield reduction of 0.075 t ha⁻¹ for the Gulliver variety cultivar during early July sowing. For the Minowase cultivar, this decrease ranged from 0.049 to 0.051 t ha⁻¹. In contrast, the impact of precipitation was positive: every additional 10 mm of moisture increased the yield of the Gulliver variety cultivar by 0.173–0.296 t ha⁻¹, with a peak increase of 3.0 t ha⁻¹ during mid-July sowing. For the Minowase variety cultivar, the yield increased by 0.229–0.531 t ha⁻¹, depending on the specific sowing date.

According to factor A (variety), growing the Minowase variety, compared to the Gulliver variety, gives a significant increase in the root crop yield at all sowing dates (

Table 4. Descriptive statistics of the yield of the daikon variety depending on the sowing dates (2022–2024).

Effect	Level of Factor	Level of Factor	Level of Factor	N	Yield, t ha−1 Mean	Yield, t ha−1 Std.Dev.	Yield, t ha−1 Std.Err	Yield, t ha−1 −95%	Yield, t ha−1 95%
Total				72	51.6	4.84	0.57	50.50	52.78
Year	2022			24	52.9	4.91	1.00	50.86	55.01
Year	2023			24	50.6	3.80	0.78	48.97	52.18
Year	2024			24	51.4	5.55	1.13	49.06	53.74
Variety	Minowase			36	53.5	4.85	0.81	51.88	55.17
Variety	Gulliver			36	49.8	4.08	0.68	48.37	51.13
Sowing dates	A			18	47.0	2.52	0.59	45.75	48.25
Sowing dates	B			18	49.1	2.95	0.70	47.66	50.60
Sowing dates	C			18	56.7	3.22	0.76	55.11	58.32
Sowing dates	D			18	53.7	3.34	0.79	52.04	55.36
Year × Variety	2022	Minowase		12	54.8	5.01	1.45	51.62	57.98
Year × Variety	2022	Gulliver		12	51.1	4.22	1.22	48.39	53.76
Year × Variety	2023	Minowase		12	52.7	3.42	0.99	50.48	54.82
Year × Variety	2023	Gulliver		12	48.5	3.02	0.87	46.58	50.42
Year × Variety	2024	Minowase		12	53.1	5.94	1.71	49.35	56.90
Year × Variety	2024	Gulliver		12	49.7	4.74	1.37	46.66	52.69
Year × Sowing dates	2022	A		6	47.2	2.46	1.00	44.62	49.78
Year × Sowing dates	2022	B		6	51.3	1.95	0.79	49.26	53.34
Year × Sowing dates	2022	C		6	58.5	3.12	1.27	55.23	61.77
Year × Sowing dates	2022	D		6	54.8	2.77	1.13	51.84	57.66
Year × Sowing dates	2023	A		6	48.9	1.81	0.74	47.00	50.80
Year × Sowing dates	2023	B		6	47.6	3.75	1.53	43.66	51.54
Year × Sowing dates	2023	C		6	54.2	2.76	1.13	51.30	57.10
Year × Sowing dates	2023	D		6	51.6	3.23	1.32	48.21	54.99
Year × Sowing dates	2024	A		6	44.9	1.63	0.66	43.19	46.61
Year × Sowing dates	2024	B		6	48.5	1.70	0.69	46.72	50.28
Year × Sowing dates	2024	C		6	57.5	2.43	0.99	54.90	60.00
Year × Sowing dates	2024	D		6	54.8	3.44	1.41	51.14	58.36
Variety × Sowing dates	Minowase	A		9	48.0	2.14	0.71	46.33	49.61
Variety × Sowing dates	Minowase	B		9	50.8	2.19	0.73	49.15	52.51
Variety × Sowing dates	Minowase	C		9	58.9	2.38	0.79	57.04	60.70
Variety × Sowing dates	Minowase	D		9	56.4	1.84	0.61	55.02	57.85
Variety × Sowing dates	Gulliver	A		9	46.0	2.62	0.87	44.02	48.05
Variety × Sowing dates	Gulliver	B		9	47.4	2.70	0.90	45.36	49.51
Variety × Sowing dates	Gulliver	C		9	54.6	2.45	0.82	52.68	56.45
Variety × Sowing dates	Gulliver	D		9	51.0	1.87	0.62	49.53	52.41
Year × Variety × Sowing dates	2022	Minowase	A	3	48.6	2.21	1.28	43.10	54.10
Year × Variety × Sowing dates	2022	Minowase	B	3	52.7	1.37	0.79	49.29	56.11
Year × Variety × Sowing dates	2022	Minowase	C	3	60.7	2.30	1.33	54.99	66.41
Year × Variety × Sowing dates	2022	Minowase	D	3	57.2	0.81	0.47	55.18	59.22
Year × Variety × Sowing dates	2022	Gulliver	A	3	45.8	2.09	1.21	40.61	50.99
Year × Variety × Sowing dates	2022	Gulliver	B	3	49.9	1.30	0.75	46.67	53.13
Year × Variety × Sowing dates	2022	Gulliver	C	3	56.3	2.13	1.23	51.01	61.59
Year × Variety × Sowing dates	2022	Gulliver	D	3	52.3	0.74	0.43	50.46	54.14
Year × Variety × Sowing dates	2023	Minowase	A	3	49.2	2.00	1.16	44.22	54.18
Year × Variety × Sowing dates	2023	Minowase	B	3	50.5	2.36	1.36	44.63	56.37
Year × Variety × Sowing dates	2023	Minowase	C	3	56.5	1.32	0.76	53.23	59.77
Year × Variety × Sowing dates	2023	Minowase	D	3	54.4	1.20	0.69	51.42	57.38
Year × Variety × Sowing dates	2023	Gulliver	A	3	48.6	1.98	1.14	43.69	53.51
Year × Variety × Sowing dates	2023	Gulliver	B	3	44.7	2.09	1.21	39.51	49.89
Year × Variety × Sowing dates	2023	Gulliver	C	3	51.9	1.21	0.70	48.89	54.91
Year × Variety × Sowing dates	2023	Gulliver	D	3	48.8	1.08	0.62	46.13	51.47
Year × Variety × Sowing dates	2024	Minowase	A	3	46.1	1.10	0.63	43.37	48.83
Year × Variety × Sowing dates	2024	Minowase	B	3	49.3	1.65	0.95	45.20	53.40
Year × Variety × Sowing dates	2024	Minowase	C	3	59.4	1.33	0.77	56.09	62.71
Year × Variety × Sowing dates	2024	Minowase	D	3	57.7	1.40	0.81	54.23	61.17
Year × Variety × Sowing dates	2024	Gulliver	A	3	43.7	1.04	0.60	41.11	46.29
Year × Variety × Sowing dates	2024	Gulliver	B	3	47.7	1.60	0.92	43.73	51.67
Year × Variety × Sowing dates	2024	Gulliver	C	3	55.5	1.24	0.72	52.41	58.59
Year × Variety × Sowing dates	2024	Gulliver	D	3	51.8	1.25	0.72	48.69	54.91

Notes: N shows the number of replications for each experimental variant (variety (Gulliver, Minowase)) n = 2; sowing date (A—First ten-day period of July, B—Second ten-day period of July, C—Third ten-day period of July, D—First ten-day period of August) n = 4; spatial replication n = 3; temporal replication (years) n = 3 (2022, 2023, 2024); Mean—average yield; Std.Dev.—standard deviation, indicating the range of data relative to the mean; Std.Err.—standard error of the mean, which characterizes the accuracy of the estimate of the sample mean; The −95.00% and +95.00% columns reflect the lower and upper limits of the 95% confidence interval, which allows estimating the range in which the true average yield for the general population lies.

, Appendix A). The lowest yield deviation of 2.0 t ha⁻¹ or 4.3% for the Minowase variety, in relation to the Gulliver variety, was noted when sowing in the first ten days of July, and the yield indicator was 48.0 t ha⁻¹. Sowing of the Minowase variety in the second and third ten-day periods of July provided a yield of 50.8–58.9 t ha⁻¹, which was characterized as an intermediate value (3.4–4.3 t ha⁻¹ or 7.2–7.9%) in deviation from the Gulliver variety. On the whole, the Minowase variety, when sown in the first ten days of August (the yield 56.4 t ha⁻¹), gives the greatest increase in the yield by 5.4 t ha⁻¹ or 10.6%, compared to the control (Gulliver variety).

The influence of factor B (sowing date) on the yield of the Gulliver variety is as follows: sowing in the first ten days of July provided a smaller increase in the yield by 1.4 t ha⁻¹ or 3.0%, compared to the control (second ten days of July) with an average yield of 46.0 t ha⁻¹; however, sowing in the third ten days of July allowed obtaining the highest yield of 54.6 t ha⁻¹, which is 7.2 t ha⁻¹ or 15.2% more than the control; sowing in the first ten days of August also provided a higher yield (51.0 t ha⁻¹) by 3.6 t ha⁻¹ or 7.6%, compared to the control.

As experience showed, the Minowase variety responded to sowing dates similarly to the Gulliver variety. When sowing the Minowase variety in the first ten days of July, the yield was 48.0 t ha⁻¹, which is 2.8 t ha⁻¹ or 5.5% less than the control variant (second ten days of July). High yield (56.4–58.9 t ha⁻¹) of the Minowase variety was achieved by sowing in the third ten-day period of July and the first ten-day period of August, which exceeded the control by 5.6–8.1 t ha⁻¹ or 11.0–15.9%. An analysis of the interaction of factors A (variety) and B (sowing date) showed that, to achieve the maximum yield of 58.9 t ha⁻¹, the best combination of these factors is sowing the Minowase variety in the third ten-day period of July.

A direct correlation was found between daikon yield and sowing dates (r = 0.57; 0.75), indicating a moderate to strong relationship. The calculated regression coefficient shows that with a 10-day shift in the sowing date, the yield for the Gulliver variety changes by 1.92 t ha⁻¹, and for the Minowase variety, by 2.99 t ha⁻¹ (Figure 5).

The obtained results showed a higher average root crop mass at all sowing dates for the Minowase variety compared to the Gulliver variety (Figure 6). When sowing in the first ten days of July, this indicator was at the control level, and the deviation relative to the control was only 3.0 g or 0.8%. A significant deviation in the average root crop mass (12.0–17.0 g or 2.9–4.5%) was ensured by sowing at a later date.

Both the earlier and the later sowing dates for daikon significantly affect the average mass of the root crop. Thus, for the Gulliver variety, the lowest average root crop mass of 371 g, which is 11.0 g or 2.9% less than the control, was achieved by sowing in the first ten days of July. The maximum average root crop mass, which reached 414 g, 32.0 g or 8.4% higher than the control (second ten-day period of July), was obtained when sowing in the third ten-day period of July. Further sowing in the first ten days of August led to a decrease in the average root crop mass to 396 g, but this figure was by 14.0 g or 3.7% higher than the figures for the control sowing period (second ten days of July).

The dependence of the average root crop mass for the Minowase variety on the sowing date also shows significant variations. The lowest value (374 g), which is 11.0 g or 2.9% less than in the control, was recorded when sowing in the first ten days of July. The highest value (426 g), which exceeded the control variant by 27.0 g or 6.8%, was obtained when sowing in the third ten-day period of July. Sowing in the first ten days of August also provided a larger root crop mass (412 g), which is 13.0 g or 3.3% more than the control.

The results indicate that the interaction of varieties and the sowing dates was significant for the marketability of the root crops (Figure 7). Regardless of the sowing date, the Minowase variety demonstrated higher marketability of the root crops, compared to the Gulliver variety. The difference in marketability of the root crops between the investigated varieties was 1.5–3.7 (%).

For the Gulliver variety, the minimum marketability of the root crops (55.9%) was observed when sowing in the second ten-day period of July, while the maximum value (59.3%) was observed in the third ten-day period of July. High marketability of the root crops (58.0%) was ensured also by sowing in the first ten days of August. The marketability of the root crops, when sown in the first ten days of July, did not differ significantly from the control.

Analysis of the Minowase variety showed that the lowest marketability of the root crops (57.4%) was observed when sowing in the second ten days of July. However, the maximum value (62.3%) was achieved when sowing in the third ten-day period of July. It is important to note that sowing in the first ten days of August also ensured a high level of marketability (61.7%). Sowing in the first ten days of July ensured the marketability of root crops at the control level.

Among the varieties presented, the Minowase variety stands out due to its better biochemical composition (dry matter 6.4–7.3%, total sugar 3.4–4.1%, vitamin C 23.5–25.8 mg per 100 g) and higher tasting score (6.5–7.2 points).

Variations in the biochemical composition of the root crops were observed at different sowing dates. When the Gulliver variety was sown in the first ten days of July, the highest dry matter (6.8%), total sugar (3.1%), and vitamin C (24.9 mg per 100 g) contents were observed, which were 0.3%, 0.3%, and 0.4 mg per 100 g higher, respectively, than in the control (second ten days of July) (Figure 8). In the control variant (the second ten-day period of July) the root crops were characterized by a slight decrease in biochemical parameters (dry matter content 6.5%, total sugar 2.8%, vitamin C 24.5 mg per 100 g), compared to the earliest sowing date. With each subsequent sowing date the negative dynamics of biochemical parameters were noted. Compared with the control (second ten-day period of July), the dry matter content decreased by 0.2–0.4%, total sugar by 0.4–0.6%, and vitamin C by 0.8–1.6 mg per 100 g. Although sowing in the first ten-day period of August shows a slight improvement in the dry matter and total sugar content, compared with the third ten-day period of July, the general trend of decreasing the quality remains unchanged. The organoleptic analysis showed that the highest tasting scores (6.7 points) were received by the root crops of the Gulliver variety, grown when sown in the third ten-day period of July.

For the Minowase variety it is possible also to highlight the sowing option in the first ten-day period of July, which was characterized by a high content of dry matter (7.3%), total sugar (4.1%) and vitamin C (25.8 mg per 100 g). Notably, these figures surpassed the results of the control sowing date, with dry matter increasing by 0.6%, total sugar by 0.2%, and vitamin C by 0.3 mg per 100 g. However, further delaying the sowing until the first ten days of August triggered a sharp decline in these indicators. A similar trend was evident in the sensory evaluation: the early July sowing secured the peak score of 7.2 points, establishing the clear superiority of this sowing window for enhancing root crop quality.

It has been established that the sowing dates have an inverse correlation with biochemical parameters and the tasting assessment. The strength of a correlation for the Gulliver variety was as follows: a strong correlation with total sugar (r = −0.68); a moderate correlation with dry matter (r = −0.54) and tasting score (r = −0.52); and a weak correlation with vitamin C (r = −0.26). For the Minowase variety the relationship was strong with dry matter (r = −0.65), total sugar (r = −0.66), vitamin C (r = −0.37) and tasting score (r = −0.48). There is an inverse relationship between the root crop mass and the biochemical indicators. The strength of relationships for the investigated varieties was as follows: a strong relationship with the content of the dry matter (r = −0.93; −0.94) and total sugar (r = −0.91; −0.93), and a medium relationship with vitamin C (r = −0.50; −0.53). A strong inverse correlation was also found between the tasting score and the average root crop mass (r = −0.83; −0.87). It should be noted that there is a strong direct relationship (r = 0.87; 0.91) between the vitamin C content and the total air temperature (more than 10 °C). At the same time, a strong linear correlation is observed between the tasting score and the dry matter content (r = 0.87; 0.95) and total sugar (r = 0.90; 0.90), while a moderate linear relationship was found with vitamin C (r = 0.34; 0.57) (Appendix B).

The regression analysis revealed that with a 10-day shift in the sowing date for the Gulliver variety (control), the dry matter content decreases by 0.16%, total sugar by 0.23%, vitamin C by 0.54 mg per 100 g, and the tasting score by 0.19 points (Figure 9). For the Minowase variety these indicators decreased by 0.23% (dry matter), 0.20% (total sugar), 0.58 mg per 100 g (vitamin C) and 0.16 points (tasting score), respectively.

Using the derived regression equations, it was found that, with an increase in the average root mass by 10 g, the dry matter content of daikon varieties decreased by 0.14% for the Gulliver variety and by 0.18% for the Minowase variety. The total sugar content decreased by 0.16% for the Minowase and Gulliver varieties. The vitamin C content decreases by 0.43 mg per 100 g in the Minowase variety and by 0.56 mg per 100 g in the Gulliver variety. The tasting score decreases by 0.15 points for the Minowase variety and by 0.16 points for the Gulliver variety (Figure 10).

At the same time it was found that an increase in the sum of the air temperatures (over 10 °C) for every 10 °C contributes to an increase in the vitamin C content by 0.06 in the Minowase variety and by 0.39 mg per 100 g in the Gulliver variety (Figure 11).

The results of the regression analysis indicate that an increase in the biochemical indicators of the daikon has a positive effect on its taste qualities (Figure 12). Thus, with an increase in the dry matter by 1%, the total sugar by 1% and vitamin C by 1 mg per 100 g, the tasting score of the Gulliver variety increases by 1.19 points, 0.97 points and 0.10 points, respectively, and for the Minowase variety, increases of 0.79 points, 0.94 points and 0.07 points, respectively.

4. Discussion

Daikon productivity and quality are primarily governed by the interaction between genetics and thermal regimes. While the optimal temperature for germination ranges from 20 to 30 °C [24], our findings confirm that the intensive growth phase requires cooler conditions (10–15.5 °C) to ensure high root quality [52]. Synchronizing sowing dates is essential to bypass mid-summer temperature peaks, which trigger physiological disorders such as branching, spongy pith, and internal browning [33].

We identified that sowing in the third ten-day period of July serves as an effective climate adaptation strategy. This timing aligns the critical root formation stage with the favorable temperatures and optimal diurnal fluctuations of September [53]. Conversely, early July sowing exposes plants to heat stress (20.0–26.6 °C) in August, forcing the redirection of carbohydrates from root storage to respiratory metabolism [54].

The transition to the generative phase is a major driver of yield loss. As a long-day plant, photoperiods exceeding 14 h stimulate flowering [55], whereas summer sowing under shortening days promotes biomass accumulation in the roots [26]. Premature bolting leads to tissue lignification and reduced root mass [56], a trait negatively correlated with total yield [57]. Understanding the physiological drivers of such changes necessitates considering the genetic factors that inhibit generative development [58]. While a direct analysis of the RsFLC2 gene in the studied cultivars was beyond the scope of this work, existing literature confirms its pivotal role as a biological safeguard. This genetic mechanism likely accounts for the reduced temperature sensitivity and prevents premature bolting in late-maturing forms [59].

The synergy between sowing date and cultivar selection is the ultimate determinant of productivity [60]. In our study, the Minowase variety achieved a maximum yield of 58.9 t ha⁻¹, significantly outperforming the Gulliver (54.6 t ha⁻¹). This regional success mirrors global trends, where optimal sowing windows vary from mid-October in India [60] and Iraq [61] to spring months in Nepal [57], emphasizing that varietal selection must be precisely synchronized with local climate dynamics [62,63,64].

Environmental factors profoundly influence the biochemical profile of daikon. We observed a strong positive correlation between carbohydrates and phenolic compounds, suggesting that sugars (primarily sucrose and glucose) serve as energy precursors for secondary metabolite synthesis [65,66]. While light intensity impacts ascorbic acid levels [67], our varieties showed lower Vitamin C values (22.9–25.8 mg per 100 g) compared to previous reports [68], with a direct correlation to the sum of active air temperatures.

From a commercial standpoint, Minowase maintained superior organoleptic properties compared to Gulliver [69]. To mitigate climatic risks, integrating crop monitoring—such as leaf area index analysis and linear regression yield models—allows for high-precision management and sustainable production [70,71,72].

Furthermore, the integration of healthy diet perspectives [73] and the selection of varieties suitable for long-term storage [74,75] can enhance the economic value of root crops. Advanced breeding strategies aimed at higher yield and better quality [76], combined with the optimized sowing dates identified in this study, provide a robust framework for the sustainable production of daikon in an era of changing climatic conditions.

5. Conclusions

This study confirms that optimizing sowing dates is a key adaptive management tool for stabilizing daikon production under increasing temperatures and growing hydrothermal variability in medium-podzolic, light loamy soil conditions. Shifting sowing dates from early July (first to second ten-day period) to late July and early August improved the synchronization of critical growth stages with cooler and more favorable moisture conditions. This shift ensured a consistent yield advantage of approximately 7.6–15.9% compared to earlier sowing dates. When sown during the third ten-day period of July to the first ten-day period of August—a period characterized by a more balanced hydrothermal regime—average yields ranged from 51.0 to 58.9 t ha⁻¹. Moisture availability during root crop formation remained the primary factor for yield stability. A strong positive correlation between yield and precipitation (r = 0.56–0.76) emphasizes that water supply is the main limiting factor under current climate change, while accumulated heat played a secondary role. These findings highlight that adaptive scheduling, rather than resource intensification, forms the basis for sustainable crop formation. Variety selection further enhanced adaptive capacity: the Minowase variety demonstrated a consistent yield advantage of 4.3–10.6%, higher marketable yield (by 1.8–3.7%), and superior biochemical quality, indicating high ecological plasticity under a changing climate. Thus, the combination of climate-adjusted sowing dates and adaptive genotypes represents an effective path toward climate-resilient vegetable production. Future research should focus on expanding agro-climatic zones, incorporating a broader genetic diversity of daikon varieties, and integrating sowing schedules with moisture management practices and projected climate scenarios to enhance the resilience of daikon production.