Evaluation of Independent Parameter Effects on Sesame (Sesamum indicum L.) Yield and Water Productivity in Tape Drip Irrigation

doi:10.21203/rs.3.rs-3002849/v1

As water scarcity becomes more acute in many parts of the world, increasing the effectiveness with which agricultural water resources are used is a priority for enhanced food security of water. Experiment was carried in the fromed out of randomized complete block design (RCBD) with three replications in Behbahan Agricultural Research Station in Khuzestan province southwest Iran, during the 2018 and 2019 growing seasons. To evaluate of Yield and Water Productivity of Sesame using Drip Tape Irrigation system, different water levels based on water requirements (40, 60, 80, and 100% of the water requirement) and three sesame varieties (Darab1, Dashtestan2, and Shevin) were considered as main plots and sub plots, respectively. According to obtained results, Comparison of the average interaction effects of irrigation and various varieties showed that the Darab1 variety, which had the best results with 100% water requirement treatment and had 73.3 seeds per capsule, 125.7 capsules per plant, 2.703 gr of 1000-seed weight, and 1314.5 kg.ha^− 1 yields, was superior and came in first place.

The analysis of the regression model's, beta coefficient and the Pearson correlation coefficient for the studied traits revealed a trend toward increasing beta coefficient and Pearson correlation coefficient values as water consumption increased from treatment 40–100% water requirement. The increasing trend in beta and Pearson correlation coefficients had a higher slope from 40 to 80% of water requirement levels and a lower slope from 80 to 100% of water requirement levels. At the level of 80% of water requirement, yield and water productivity had the highest beta coefficients (0.622 and 0.633) and Pearson correlation (0.712 ** and 0.730 **) with capsules per plant, respectively. Less fluctuation of beta and Pearson correlation coefficients from the level of 80 to 100% of water requirement compared to other irrigation levels caused the level of 80% of water requirement to be introduced as the optimal level of irrigation, and in conclusion, the highest water productivity was observed in Darab1 variety. Additionally, this research highlights the possibility of using Darab1 variety in study area and region with similar conditions.

1000-seed weight

water use efficiency

variety

evaporation and transpiration

Drought stress is the most significant environmental element preventing plant growth and development on a global scale. Therefore, compared to other environmental pressures, drought stress has been more strongly associated with slower development rates (Aien, 2013). Iran is not immune to the negative consequences of this climatic phenomenon, as crops experience water deficit in half of the country's arable area (Seydan and Ghadami Firouzabadi, 2002). So, improving irrigation techniques and using drought-tolerant cultivars with high water productivity are two crucial steps to take in order to maximize the use of water resources and boost the productivity of already-existing resources (Kimber and McGregor, 1995, Gholamhoseini, 2020). Due to its ability to withstand both heat and drought, sesame (Sesamum indicum L.) is a crucial summer crop for the growth of agriculture in arid and semi-arid areas (Aien, 2013, Dossa et al.,2017). The seeds of sesame are commonly used for food consumption and for culinary use worldwide, primarily due to its high fat and high protein contents. The high oil content in sesame seeds, varying between 35 and 63%, makes it a major source of cooking oil (Pabuayon et al., 2019; Kadkhodaie et al., 2014).

In the other words, Oilseeds have a special position among crops in terms of providing calories and energy needed by humans and animals (Zafar, et al., 2019). So that, today oilseeds are considered as the second most important source of energy supply after cereals.

The findings of Shokoufar and Yaghoobinejad (2012) demonstrated that cultivars that were more sensitive to stress (Panama, TS-3, and Varamin 2822) in this study, drought stress affects yield components at a probability level of 5%. Although it was found that some traits, like capsule length, under stress conditions and normal irrigation conditions did not show significantly different results, their yield reduction conditions were more severe. Khani et al (2011) findings demonstrated that the interaction impact of treatments on grain yield, number of capsules per plant, and number of grains per capsule was statistically significant at the 1% level and that yield declined with increasing drought stress. The Darab 14 cultivar was able to produce more resistance than other cultivars, resulting in a rise in its 1,000 seed weight. Therefore, finally, they introduced the Darab 14 cultivar as a resistant cultivar to water scarcity.

According to the findings of Eskandari et al. (2010), plant height, leaf count, biological yield, and grain production per unit area all declined as the severity of the water shortage increased. The cultivar TS3 achieved the highest water productivity. Drought stress at the flowering stage had a substantial negative influence on plant height, capsule size, number of seeds per capsule, and seed weight based on research by Dossa et al. (2019) on the impact of drought stress on growth and yield related to sesame production. Kumar et al. (1996) indicated that drought stress reduces leaf area, number of capsules per plant, 1000-seed weight and sesame seed yield.

Sakila et al. (2000) found a positive and significant correlation between plant height, the number of capsules on each main stem, and the overall number of capsules on a plant and single plant yield. These researchers think that selection can be structured based on the number of capsules per plant and plant height in order to increase sesame output because there was a positive and significant correlation between the two variables. In another research, six drip irrigations per sesame crop produced the maximum output and traits, while five drip irrigations and the removal of one irrigation at the start of the sesame flowering stage produced the lowest values. For three, four, five, and six irrigations, respectively, the corresponding ETc values for all growth stages were 1323, 1382, 1487, and 1647 m³ per 4600 m². The greatest water productivity and 350 kg of seeds per m³ of water were achieved with six irrigations. As a result, applying six irrigation regimes to sesame under drought- and stress-free conditions produced the maximum yield while using the least amount of water (El-Wakil & Gaafar, 1985). Researchers found that among the yield components, drought stress considerably decreased the number of capsules per plant and the number of seeds per capsule. The irrigation system also had a considerable negative impact on grain yield. Sesame seed output reduces under drought stress conditions due to factors like the number of capsules per plant and the number of seeds per capsule (Mehrabi and Ehsan Zadeh, 2010). The analysis of variance results revealed that cultivars and irrigation regimes significantly differed in terms of grain yield and water productivity. The interaction impact of irrigation and cultivar was substantial in terms of water productivity (Dargahi et al, 2014). Under full irrigation, sesame produced the largest grain yield; as drought stress increased, grain output declined (Dargahi et al, 2014, Kassab et al, 2012 and Shokoufar and Yaghoobinejad, 2012). In a another study, drip irrigation was used with the three cultivars Giza 32, Toshky 1, and Shandweel 3 and the three irrigation levels of 50, 75, and 100% ET₀. The study found that the 100 percentage of evapotranspiration irrigation level provided the highest water productivity for sesame seed under full irrigation conditions (Kassab et al., 2012).

According to Saeidi et al. (2012), sesame seed yield reduces as drought stress increases and the Darab 14 and Sirjan cultivars were found to have the most seeds per capsule and the most capsules per plant during their testing. The findings of a study conducted in India revealed a substantial and positive association between yield and plant dry matter and 1000-seed weight (El-Serogy et al, 1997). Researchers haven't yet given much thought to the significance of water efficiency and the timing of corn cultivation and sesame flowering, which could lead to corn receiving more water during sesame flowering, particularly in drip irrigation. Because of this, recent study has revealed that the impacts of various drip irrigation levels, particularly during the flowering stage, on various sesame cultivars have not been investigated or the findings of such investigations have not been published. Furthermore, under extreme stress at a level of 40% water requirement during blooming, which may be brought on by water allocation to other crops, how will the plant respond at this level of water requirement and how much will the reducing effect reduce the yield? Which of the yield factors will have the biggest effect on water output and productivity at each drip irrigation level? There are currently no published research reports. Due to the significance of sesame as one of Iran's main sources of edible oil, water limitations in the case of a drought or reduced water allotment can be accommodated by employing the deficit irrigation method. This present study aimed was to identify and introduce the most significant independent variable among yield components that could have the biggest impact on sesame yield and water use efficiency before applying various levels of tape drip irrigation to yield components to investigate the effects of drought stress in semiarid region southwest Iran.

2.1 Study site

Behbahan is located in khuzestan province, southweth of Iran(Fig. 1). present field trail were performed in 2018 and 2019 growing seasons on the research farms of the Behbahan Agricultural Research Station, khuzestan, Iran(30° 36՜ N, 50° 14՜ E, 345 m asl) on the silty clay loam(SCL) soil texture type. The study area has a semi-arid climate, with an annual mean rainfall of 349 mm, and ustic and hyperthermic soil moisture and temperature regiems, respectively.

Figure 1 geographical location of the Study area in Khuzestan Province, Iran

2.2 Experimental Desing. Experiment was laid out in RCBD with three replications. The primary factor was the amount of water used in drip irrigation, which was divided into four levels based on water requirements ofirrigation amount( 40%, 60%, 80%, and 100% ETc) from the start of the flowering stage. The secondary factors were Darab1, Dashtestan2, and Shevin cultivars, which were divided into three levels. Before planting, a soil sample was taken for testing. Additionally, a water sample from the irrigation water used during the season was made and sent to the lab to be measured for the appropriate qualities. The results of soil and water sample analysis are presented in Tables 1 and 2.

Table 1

quality of the water used for irrigation
Year	EC (µs.m^− 1)	pH	T.D.S Mg.lit^− 1	Anions (meq.l ^− 1)			Cations (meq.l^− 1 )
Year	EC (µs.m^− 1)	pH	T.D.S Mg.lit^− 1	cl⁻	So4^− 2	HCO₃	Na⁺	Mg^+ 2	Ca^+ 2
2018	1740	7.4	1140	8.8	8.0	3.2	8.0	3.2	8.8
2019	1680	7.4	1140	8.7	8.1	3.2	7.9	3.2	8.8

Table 2

Physical and chemical properties of soil samples before planting
Year	Soil depth	FC	\(pb\)	K	P	OC	pH	EC	Soil texture
Year	(cm)	(%)	)gr.cm^− 3)	(mg.kg^− 1)		(%)		(ds.m^− 1)	Soil texture
2018	0–30	24	1.57	246	9.5	0.64	7.5	2.8	SCL
2019	0–30	24	1.57	250	9.2	0.66	7.5	3.0	SCL

Table 1 quality of the water used for irrigation

Table 2 Physical and chemical properties of soil samples before planting

There were two ridges and four planting lines on each sub-plot. Each ridge has a width of 90 cm, a length of 20 meters, and an area of 36 square meters. An area of 12 square meters was taken at the bottom of each row. The primary plots were said to be separated by four meters. The subplots were not stacked one on top of the other, and there were 15 cm between each plant in the row. After plowing, fertilizer and terflan herbicide were applied at a rate of 2.5 liters per hectare in a uniform distribution, and the fertilizer and herbicide were blended with the soil using a light disc. Based on a soil test, fertilizers for phosphorus and potassium were applied prior to planting at rates of 110 kg.ha^− 1 of triple superphosphate and 100 kg.ha^− 1of potassium sulfate. In addition, three applications of nitrogen fertilizer from a source of urea were made, with the third application occurring at the same time as the second irrigation. Following the thinning, one third was applied, and the other third was applied at the start of the flowering stage (100 kg.ha^− 1).

The ETo Calculator program was used to determine plant evapotranspiration daily based on the Penman-MMonteith model using the long-term daily statistics of the previous ten years at the Behbahan Synoptic Meteorological Station (minimum and maximum daily temperature, minimum and maximum daily humidity, wind speed, and maximum sunny hours). Based on the FAO 56 model, plant coefficients were calculated (Allen et al, 1998). Daily irrigation time was computed using the data under observation, and weight moisture, volumetric moisture, and ultimately soil moisture deficiency were evaluated by sampling the soil prior to several irrigations. Validated. The plant's daily water requirement was therefore determined to be 100%. Next, the percentages of water requirements for treatments at 40, 60, 80, and 100% were computed. The water supplied to the facility was then measured with an accuracy of 0.0001 cubic meters. FAO 29 states that the following equation was used to determine how much water was needed for leaching on the farms under study that used drip irrigation (FAO, 2005):

LR = ECw /(2Max ECe) (1)

where ECe is the product salinity tolerance threshold, MaxECe is the salinity with zero performance, and ECw is the electrical conductivity of irrigation water (in dS.m^− 1). The leaching requirement was first estimated using Formula (1) in terms of a percentage, and then, taking into account the water demand determined during the sesame growing season, it was computed in terms of millimeters. For the goods under consideration, salinity tolerance criteria were extracted using FAO 29 with a 100% yield reduction. The efficiency of seasonal irrigation was projected to be 95%. Calculations were made to determine how much water was needed for sesame during the growing and harvesting seasons from planting to harvest from July 27 to November 13, 2018 for the first year, and from July 24 to November 10, 2019 for the second year (Table 3). By dividing the yield by the total amount of water used for irrigation and actual rainfall, water productivity was estimated (Eq. 2).

WUE = Y/(Pe + Ir) (2)

The United States Department of Agriculture (USDA) technique was used to calculate the effective rainfall in Behbahan City across four crop years. With a probability of 65% of the following relationships, the effective precipitation values in the monthly time period were computed using this approach (Marie, 2015). The effective rainfall levels for the fodder corn plants in Behbahan city were computed for the monthly period using the aforementioned method. Two relationships are provided in this method for rainfall above and below 250 mm. Calculations were done using relation 3 because the studied locations had monthly rainfall totals of no more than 250 mm.

Pe = P×((125-(0.2×P))/125) P < 250 mm (3)

Pe = 0.1P + 125 P > 250 mm (4)

Pe stands for effective rainfall (mm) in the relationships above, and P stands for monthly rainfall. Y stands for grain yield (kg), Pe for effective rainfall (mm), Ir for irrigation depth (mm), and P for monthly rainfall. WUE is for water productivity (kg.m^− 3). Figure 2 depicts the daily precipitation during the sesame growing season and the first and second years of the experiment (A and B).

Figure 2 Precipitation during plant growth, first year (A) and second year (B)

Table 3 Values of water used in experimental treatments for two years of testing (m³.ha^− 1)

Table 3

Values of water used in experimental treatments for two years of testing (m³.ha^− 1)
Year	2018				2019
Month	40% water requirement	60% water requirement	80% water requirement	100% water requirement	40% water requirement	60% water requirement	80% water requirement	100% water requirement
July	184.6	184.6	184.6	184.6	265.4	265.4	265.4	265.4
August	1248.6	1248.6	1248.6	1248.6	1380.2	1380.2	1380.2	1380.2
September	2183.0	1903.6	1624.3	1345.0	2116.9	1802.4	1487.9	1173.4
October	1587.7	1270.1	952.6	635.1	1479.3	1183.5	887.6	591.7
November	213.1	170.4	127.8	85.2	120.0	96.0	72.0	48.0
Total	3498.4	4137.9	4777.4	5416.8	3458.7	4093.1	4727.4	5361.8

During the growing season, characteristics including grain per capsule, capsule per plant, 1000-seed weight, grain yield, and water use efficiency were recorded or computed. Based on the RCBD in the form of split plots for these traits, analysis of variance was carried out at the conclusion of the experiment. The Duncan test was used to compare means throughout the statistical analysis, which was done using the SPSS 16 program. The results of the measured or estimated parameters were statistically compared using Pearson correlation coefficient between yield indicators, yield components, and water productivity at levels of 1 and 5% was used to examine the results. For linear multivariate regression, SPSS 16 software was utilized. The aforementioned study was carried out to identify the equation for calculating sesame yield and water productivity as a dependent variable and to explain variations in sesame yield components (1000-seed weight, grain per capsule, and capsule per plant) as independent variables.

The analysis of variance revealed that the effects of irrigation levels, cultivar, and their interactions were significant at the 1% level for the number of seeds per capsule, the number of capsules per plant, the weight of 1000 seeds, yield, and water use efficiency (Table 4). These findings matched those of the study conducted by Dargahi et al., (2014); Khani et al., (2011); Shokoufar and Yaghoobinejad, (2012).

Table 4

Comparison of mean squares and significance level of yield and yield components and water productivity
Sources of changes	Degrees of freedom	the number of grains per capsule	Number of capsules per plant	the weight of one thousand seeds	yield	water productivity
Year	1	105.7 ^ns	244.0 ^ns	0.0398 ^ns	7050.9 ^ns	0.001 ^ns
Repeat	4	2.5 ^ns	4.9 ^ns	0.0036 ^ns	976.8 ^ns	0.000 ^ns
Irrigation	3	2788.3**	6505.7**	3.8363**	1006427.9**	0.008**
Year * Irrigation	3	1.7 ^ns	2.5 ^ns	0.0008 ^ns	198.4 ^ns	0.000 ^ns
Error	12	3.8	8.1	0.0062	2786.7	0.000
Variety	2	520.2**	1907.6**	0.7298**	220145.5**	0.009**
Year * Variety	2	0.1 ^ns	2.2 ^ns	0.0000 ^ns	7.2 ^ns	0.000 ^ns
Variety * Irrigation	6	52.1**	129.9**	0.1041**	52175.9**	0.002**
Year * Irrigation* Variety	6	0.0 ^ns	0.1 ^ns	0.0000 ^ns	5.4 ^ns	0.000 ^ns
Error	32	4.9	7.8	0.0097	3082.8	0.000
Coefficient of variation		4.06	2.95	5.097	6.83	7.55
*:Significant difference at 1% level, :Significant difference at 5% level, ns: There was no significant difference

Table 4 Comparison of mean squares and significance level of yield and yield components and water productivity

According to Table 5's findings and a comparison of the average interactions between yield and irrigation, the Darab1 cultivar's 100% water requirement treatment, which had a yield of 1314.5 kg.ha^− 1 and contained 73.3 seeds per capsule, 125.7 capsules per plant, and 2.703 grams of 1000-seed weight, was the best. Treatments that used 100% and 80% of the water needed in Darab1 yielded, respectively, 0.244 and 0.222 cubic meters per hectare, which did not differ considerably in terms of water usage efficiency (Table 5). Results from Table (5) and analysis of the yield and water productivity columns showed that the superior yielding Darab1 consumed an average of 5378.3 and 4748.6 m³. ha^− 1 of water over two years in treatments using 100 and 80% of the water requirements. In comparison to treating 100% of the water demand, the treatment of 80% of the water requirement consumed less water, or 638.6 m³.ha^− 1, which resulted in a decrease in yield of 260.3 kg.ha^− 1. In other words, the yield was lowered by 19.8% due to the 11.8% decrease in water needed for the 100% water requirement treatment. As a result, by accepting a 19.8% decrease in yield, 11.8% of water usage can be reduced in years of low rainfall and drought (Table 5). In line with the findings of Kasab et al., (12) reducing water use did not reduce water productivity. On the other side, cutting back on water use decreased grain yield. which was in line with the study's findings (Dargahi et al., 2014; Kassab et al., 2012; Shokoufar and Yaghoobinejad, 2012;).

Table 5

Comparison of the mean of some quantitative traits and water productivity on interactions between irrigation levels and cultivar
Interaction of treatments		the number of grains per capsule	Number of capsules per plant	the weight of one thousand seeds (gr)	yield (kg.ha^− 1)	water productivity (kg.m^− 3)
Irrigation levels	Variety	the number of grains per capsule	Number of capsules per plant	the weight of one thousand seeds (gr)	yield (kg.ha^− 1)	water productivity (kg.m^− 3)
40% water requirement	Daarab1	48.0h	86.2e	1.374ef	517.7i	0.149de
	dashtestan2	32.7i	60.0f	1.286f	497.6i	0.143e
	Shevin	33.8i	61.7f	1.507de	589.4hi	0.169bcde
60% water requirement	Daarab1	54.8ef	96.7c	2.037c	787.6ef	0.191b
	dashtestan2	49.9gh	87.6de	1.621d	678.2gh	0.165cde
	Shevin	52.1fg	91.8d	1.637d	705.8fg	0.172bcd
80% water requirement	Daarab1	62.4bc	109.7b	2.404b	1054.2b	0.222a
	dashtestan2	57.1de	96.9c	1.966c	811.1e	0.171bcd
	Shevin	59.4cd	100.4c	1.986c	861.0de	0.181bc
100% water requirement	Daarab1	73.3a	125.7a	2.703a	1314.5a	0.244a
	dashtestan2	63.6b	106.6b	2.309b	940.1cd	0.174bc
	Shevin	65.2b	109.8b	2.377b	996.6bc	0.185bc
Numbers followed by the same letter are not significantly differentness (P < 0.05)

Table 5 Comparison of the mean of some quantitative traits and water productivity on interactions between irrigation levels and cultivar

Table 6 displays the findings of the regression model's analysis of variance, and Table 7 displays the coefficients of the variables that make up the regression equation for the performance trait. The findings revealed that the analyzed factors for the yield trait at a level of 40% water demand were: number of seeds per capsule, number of capsules per plant, and weight of 1000 seeds: 15.6% (R² = 0.156). Sesame yield's degree of variance, the regression's lack of significance, and the variables' linear relationship to one another were all explained (P < 0.05) (Table 6). Table 7 displays the equations taken from the final multivariate regression model for the water requirements of 40, 60, 80, and 100%. According to Table 6, the number of seeds per capsule had a negative effect on sesame yield while the variables number of capsules per plant and 1000-seed weight had positive effects (Table 6). As a result, the number of seeds per capsule's absolute beta coefficient (Beta) was negative (1.100), which was higher than the beta coefficients of the other two variables (Table 7). At a 60% water demand, the examined variables (number of seeds per capsule, number of capsules per plant, and weight of 1000 seeds) explained 0.47% (R² = 0.470), 64.9% (R² = 0.649), and 80.9% (R² = 0.809) of the variations in the sesame yield. The influence of grain on the capsule was considerable at the 5% level at a 60% water requirements, while the effect of the other two parameters was not (Table 6).

At a 40% water requirement level, the grain beta coefficient's detrimental impact on yield was reversed. The effect of the capsule beta coefficient per plant on yield was adverse (-0.131). However, this impact was insignificant. Regression significance and the linear association between the variables were also established at the level of 80% of the water requirement (P < 0.01) (Table 6). The effect of 1000-seed weight had a significant positive effect at the level of 1%, with the capsule beta coefficient per plant having the highest favorable impact on yield (0.622). In other words, the two parameters of capsule per plant and 1000-seed weight had the most significant positive influence at the level of 1% on grain yield at the level of 80% water demand. Regression significance and the linear association between the variables were also established at 100% water requirement (P < 0.01) (P 6). At this point, the only influence on grain yield that was noteworthy was 1,000 seed weight at a 5% level. The other two characteristics had no discernible impact on grain yield. However, at the level of 1%, a linear relationship between variables and significant regression were brought about by the positive beta coefficient of three independent parameters. This means that the combined impact of all three variables produced a significant regression and a linear association between variables at the level of 1% (Tables 6 and 7). At this point, the most notable beneficial impact on yield was provided by the beta coefficient of 1000-seed weight (0.465) (Table 7).

Table 6 Analysis of variance in regression model for yield adjective

Table 7 Coefficients of variables in the regression equation for yield adjective

Table 6

Analysis of variance in regression model for yield adjective
Irrigation levels	Sources Change	DF	Mean squares	F Value	The regression coefficient	R²	Corrected R²	. Sig
40% water requirement	Model	3	7310.119	.862	0.395	0.156	-0.025	0.484 ns
	Error	14	8485.244
	Total	17
60% water requirement	Model	3	12174.431	4.142	0.686	0.470	0.357	0.027 *
	Error	14	2939.011
	Total	17
80% water requirement	Model	3	33276.722	11.537	0.863	0.745	0.649	0.000**
	Error	14	2884.232
	Total	17
100% water requirement	Model	3	74081.496	19.703	0.899	0.809	0.787	0.000**
	Error	14	3759.816
	Total	17
:** Significant difference at 1% level, *: Significant difference at 5% level, ns: There was no significant difference.

Table 7

Coefficients of variables in the regression equation for yield adjective
Irrigation levels	Model	Unstandardized Coefficients		Standardized Coefficients	t Value	Sig.
Irrigation levels	Model	B coefficient	The standard error	Beta	t Value	Sig.
40% water requirement	constant number	460.301	350.728	-	1.312	0.210
	the number of grains per capsule = X₁	-15.878	11.148	-1.100	-1.424	0.176 ns
	Number of capsules per plant = X₂	8.707	5.926	1.091	1.469	0.164 ns
	1000-grain weight = X₃	54.669	178.085	.091	.307	0.763 ns
Y = 460.301–15.878 X₁ + 8.707 X₂ + 178.085 X₃
60% water requirement	constant number	-20.075	375.846	-	− .053	0.958
	the number of grains per capsule = X₁	15.088	6.211	.613	2.429	0.029 *
	Number of capsules per plant = X₂	-2.627	5.811	− .131	− .452	0.658 ns
	1000-grain weight = X₃	111.499	107.520	.265	1.037	0.317 ns
Y=-20.075 + 15.088X₁+-2.627X₂ + 111.499X₃
80% water requirement	constant number	-311.629	309.953	-	-1.005	0.332
	the number of grains per capsule = X₁	-7.275	6.419	− .224	-1.133	0.469 ns
	Number of capsules per plant = X₂	11.004	2.991	.622	3.680	0.002 **
	1000-grain weight = X₃	246.127	79.238	.562	3.106	0.008 **
Y=-311.629-7.275X₁ + 11.004X₂ + 246.127X₃
100% water requirement	constant number	-902.353	277.733	-	.279	0.006
	the number of grains per capsule = X₁	9.583	6.233	.279	.241	0.146 ns
	Number of capsules per plant = X₂	4.264	3.291	.241	.465	0.216 ns
	1000-grain weight = X₃	342.633	153.159	.465	.279	0.042 *
Y=-902.353 + 9.583X₁ + 4.264X₂ + 342.633X₃
:** Significant difference at 1% level, *: Significant difference at 5% level, ns: There was no significant difference.

Table 8 displays the findings of the regression model's analysis of variance, and Table 9 displays the coefficients of the variables in the regression equation for the attribute of water use efficiency. The results showed that at a level of 40% water need, the analyzed variables in the attribute of water productivity were the number of seeds per capsule, the number of capsules per plant, and the weight of 1000 seeds, which accounted for 15.6% of the total (R² = 0.156). Water productivity's degree of fluctuation, as well as the regression's non-significance and the variables' linear relationship, were all explained (P < 0.05). (Table 8). Table 9 displays the equations taken from the final multivariate regression model for the water requirements of 40, 60, 80, and 100%. None of the independent characteristics had a substantial impact on water production at the 40% water requirement level. Water production was negatively impacted by the number of seeds per capsule and positively impacted by the number of capsules per plant. Since the beta coefficients for the number of seeds per capsule, which is equivalent to (-0.098), and the number of capsules per plant, which is comparable to (1.089) (Table 9). The studied variables (number of capsules per plant, number of seeds per capsule, and weight of 1000 seeds) explained 48.8% (R² = 0.488) of the variance in the dependent variable sesame water productivity at the level of 60% water requirement, 72.7% (R² = 0.727) at 80% water requirement, and 82.5% (R² = 0.825) at 100% water requirement. The effect of grain per capsule was significant at the 5% level at a water requirement of 60%, while the influence of the other two parameters was no longer significant. Regression significance and a linear relationship between the variables were also discovered at 80% water requirement (P < 0.01).

The maximum 1000-seed weight (0.543) and capsule beta coefficient per plant (0.633) both significantly improved water productivity at the 1% level. The significance of regression and the linear relationship between variables were likewise established at the level of 100% water requirement (P 0.01), and the derived beta coefficient for each of the three independent factors was favorable for water productivity. At the level of 1%, this additive effect of all three components led to substantial regression and linear relationships between variables. The grain weight beta coefficient had the greatest positive impact on water productivity at this point (0.439) (Tables 8 and 9).

The results of Tables 6, 7, 8, and 9 at all irrigation levels clearly demonstrated that the independent variables of grain in capsule, grain in capsule, capsule in plant, and 1000-seed weight were the most sensitive at levels 40, 60, 80, and 100% of water requirements. The beta coefficients for the aforementioned variables are equivalent to -1.100, 0.613, 0.622, and 0.465 for performance, and − 0.098, 0.614, 0.633, and 0.439 for water productivity, respectively. In other words, intense anxiety the grain in the capsule exhibited the greatest reduction in 40% of the water requirements, and mild stress also had the strongest lowering effects. Low stress and a 60% water requirement, no stress and an 80% water requirement Grain characteristics per capsule, capsule per plant, and 1000-seed weight all showed 100% water requirements. The findings demonstrated that the independent variable with the largest absolute value of the beta coefficient in comparison to other variables was the one that was the most sensitive to changes in yield and water productivity. It was clear how cutting back on water productivity affected the beta coefficient. The beta coefficient in the grain numerical capsule turned negative as a result of extreme stress during the treatment of the 40% water requirement. In order to alleviate severe stress, water productivity was reduced to 40% of the required amount. This had such a profound impact that it caused the beta coefficient grain in the capsule, which was positive at levels of 80 and 60, to change to a negative value. Therefore, by consuming less water and lowering the beta coefficient, the effect of yield component dropping was made visible.

In accordance with the findings of Sakila et al. (2000) and Mehrabi and Ehsan Zadeh, (2010) the drop in water productivity resulted in a fall in beta coefficient, which in turn produced a decrease in yield and a decrease in water productivity. According to research findings by (Dargahi et al., 2014; Saeidi et. al., 2012; Kassab et al., 2012; Shokoufar and Yaghoobinejad, 2012;), reduced water consumption at different levels of drought stress led to fewer grain per capsule, capsule per plant, and 1000-grain weight, resulting in lower yield and water productivity.

Table 8 Analysis of variance in regression model for water use efficiency adjective

Table 9 Coefficients of variables in the regression equation for water use efficiency adjective

Table 8

Analysis of variance in regression model for water use efficiency adjective
Irrigation levels	Sources Change	DF	Mean squares	F Value	The regression coefficient	R²	Corrected R²	Sig.
40% water requirement	Model	3	.001	.861	0.395	0.156	-0.025	0.484 ^ns
	Error	14	.001
	Total	17
60% water requirement	Model	3	.001	4.452	0.699	0.488	0.379	0.021 *
	Error	14	.000
	Total	17
80% water requirement	Model	3	.002	12.447	0.853	0.727	0.669	0.000**
	Error	14	.000
	Total	17
100% water requirement	Model	3	.003	22.002	0.908	0.825	0.788	0.000**
	Error	14	.000
	Total	17
:** Significant difference at 1% level, *: Significant difference at 5% level, ns: There was no significant difference.

Table 9

Coefficients of variables in the regression equation for water use efficiency adjective
Irrigation levels	Model	Unstandardized Coefficients		Standardized Coefficients	t Value	Sig.
Irrigation levels	Model	B coefficient	The standard error	Beta	t Value	Sig.
40% water requirement	constant number	.129	.101	-	1.274	0.223
	the number of grains per capsule = X₁	− .005	.003	-1.098	-1.409	0.181 ns
	Number of capsules per plant = X₂	.003	.002	1.089	1.466	0.165 ns
	1000-grain weight = X₃	.017	.051	.099	.335	0.743 ns
Y = .129-.005X₁ + .003X₂ + .017X₃
60% water requirement	constant number	− .024	.091	-	− .265	0.795
	the number of grains per capsule = X₁	.004	.001	.614	2.476	0.027 *
	Number of capsules per plant = X₂	.000	.001	− .082	− .286	0.779 ns
	1000-grain weight = X₃	.024	.026	.236	.938	0.364 ns
Y=-.024-.004X₁ + .000X₂ + .024X₃
80% water requirement	constant number	− .079	.064	-	-1.239	0.236
	the number of grains per capsule = X₁	− .001	.001	− .196	-1.018	0.326 ns
	Number of capsules per plant = X₂	.002	.001	.633	3.846	0.002 **
	1000-grain weight = X₃	.050	.016	.543	3.081	0.008 **
Y=-.079-.001X₁ + .002X₂ + .015X₃
100% water requirement	constant number	− .176	.050	-	-3.557	0.003
	the number of grains per capsule = X₁	.002	.001	.301	1.736	0.105 ns
	Number of capsules per plant = X₂	.001	.001	.257	1.448	0.170 ns
	1000-grain weight = X₃	.060	.027	.439	2.210	0.044 *
Y=-.176-.002X₁ + .001X₂ + .060X₃
:** Significant difference at 1% level, *: Significant difference at 5% level, ns: There was no significant difference.

The independent variables were negatively impacted when the amount of water used in the drip irrigation method was decreased from 80–40% at various levels of water requirement. Reduced yield components had a significant impact on yield, which in turn had a negative impact on water productivity. As a result, limiting the amount of water used to grow sesame has decreased yield and water productivity, which is consistent with study findings by Jain et al. (2010), Kumar et al,(1996), and Eskandari et al, (2010). When the assessed attributes in Table 10 were correlated using the Pearson correlation coefficient, it was determined that:

There was no statistically significant association between yield and water productivity and any of the independent factors at a 40% water requirement. With the grain variable in the capsule per plant, the highest significant correlation coefficient of yield and water productivity was calculated at 60% of the water requirement at rates of 0.655 and 0.674, 80% of the water requirement at rates of 0.712 and 0.730, and 100% of the water requirement at rates of 0.858 and 0.861 with 1000-seed weight. The yield and water productivity at levels of 60, 80, and 100% of the required water supply, respectively, had positive and significant correlations with the aforementioned variables at the 1% level (Table 10). The investigated independent factors had a very substantial impact on yield and water productivity in the non-stress phase, which required 100% water. The overall correlation coefficient therefore exceeded 0.783 (Table 10). In other words, decreased water productivity at various irrigation levels led to decreasing trends in the Pearson correlation coefficients of 1000-grain weight, grain per capsule, and capsule per plant (Table 9). This strong link between yield and yield components under mild stress (80% water requirements) and no stress (100% water requirement) conditions demonstrated the value of yield components, such as 1000-seed weight, in boosting sesame production. The research by Hassanzadeh et al., (2009) and El-Serogy et al.,(1997) revealed that 1000-grain weight and yield components were crucial for boosting grain output. As a result, drought stress and deficit irrigation consistently decreased sesame yield and yield components.

Without stressing 100% water requirements, the strongest correlation of 1000-grain weight with grain production was estimated as r = 0.858 **, suggesting the very effective role of 1000-grain weight in enhancing grain yield (Table 10). According to the research's findings, all yield factors at 100% water requirements and no stress level had positive correlations with one another, suggesting that reducing any one of these factors could harm sesame yield in the field. (Kumar et al, 1996), (Eskandari et al, 2010). The sesame plant is sensitive to deficit irrigation stress, so it is important to pay attention to optimal water management in sesame cultivation, according to a positive and significant correlation between water productivity, in which the amount of water consumed is hidden, and grain yield components (Table 10). Therefore, applying deficit irrigation in sesame has lowered yield and yield components, which is consistent with the findings of research by (Jain et al, 2010), (Kumar et al, 1996), (Eskandari et al, 2010) and as a result, the development of drought stress.

Table 10 Pearson correlation coefficient calculated for the studied traits

Table 10

Pearson correlation coefficient calculated for the studied traits
Irrigation levels	N = 18 5%=0.590 1%=0.468	Seed yield )kg/ha(	Seeds in a capsule	Capsule in the bush	1000-grain weight (gr)	water productivity (kg/m³)
40% water requirement	Seed yield )kg/ha(	1.000	-0.120	0.009	0.154	0.999^**
	Seeds in a capsule		1.000	0.943^**	-0.551	-0.116
	Capsule in the bush			1.000	-0.497	0.013
	1000-grain weight (gr)				1.000	-0.358
	water use efficiency (kg/m³)					1.000
60% water requirement	Seed yield )kg/ha(	1.000	0.655^**	0.426	0.470	0.998^**
	Seeds in a capsule		1.000	0.631^**	0.472	0.674^**
	Capsule in the bush			1.000	0.643^**	0.458^**
	1000-grain weight (gr)				1.000	0.473
	water productivity (kg/m³)					1.000
80% water requirement	Seed yield )kg/ha(	1.000	0.444	0.712^**	0.656^**	0.999^**
	Seeds in a capsule		1.000	0.526^*	0.607^**	0.466
	Capsule in the bush			1.000	0.369	0.730^**
	1000-grain weight (gr)				1.000	0.657^**
	water productivity (kg/m³)					1.000
100% water requirement	Seed yield )kg/ha(	1.000	0.790^**	0.783^**	0.858^**	0.999^**
	Seeds in a capsule		1.000	0.671^**	0.749^**	0.803^**
	Capsule in the bush			1.000	0.763^**	0.795^**
	1000-grain weight (gr)				1.000	0.861^**
	water productivity (kg/m³)					1.000

The analysis of variance revealed that the effects of irrigation levels, cultivar, and their interactions were significant at the 1% level for the number of seeds per capsule, the number of capsules per plant, the weight of 1000 seeds, yield, and water use efficiency. The independent variables under investigation—grain per capsule, capsule per plant, and 1,000-seed weight—exhibited varying responses at each stage of various levels of irrigation shortage. The number of grains per capsule had a negative impact on water yield and water use efficiency at a level of 40% water requirement, according to the findings of the analysis of variance in the regression model for grain yield and water use efficiency. At water requirements of 40 and 60 percent, the grain in the capsule was the most significant independent factor impacting yield and water use efficiency. Capsules per plant and 1000-seed weight, respectively, were also presented as the most significant independent characteristics affecting yield and water productivity at the levels of 80% and 100% of water requirement. Due to its better yield and more water productivity, the Darab1 variety was presented as the best variety, and a level of 80% water requirement was introduced as the ideal level of irrigation. In general, according to the results obtained in this research, the Darab1 cultivar has a higher relative resistance to drought stress, so this cultivar is suggested for this region and similar conditions.

.

Acknowledgment

We appreciate the Khuzestan Agricultural and Natural Resources Research and Education Center taking the financial burden and providing moral assistance for this study.

Funding

The author(s) received no specific funding for this work.

Data Availability Statement

All relevant data are available in the paper and supplementary data. The readers should contact the corresponding author for more details.

Conflict of interest The authors declare no conflict of interest.

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No competing interests reported.

Evaluation of Independent Parameter Effects on Sesame (Sesamum indicum L.) Yield and Water Productivity in Tape Drip Irrigation

Status:

Version 1

Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Study site

3 Results and Discussion

4 Conclusion

Declarations

References

Additional Declarations

Status:

Version 1