The effect of seed and nitrogen-phosphorous fertilizer rates on growth and yield components of bread wheat (Triticum aestivum L.) in Burie District, Northwestern Ethiopia: Dataset article

Abstract

In the absence of a recommended optimal seed rate and nitrogen-phosphorous (NP) fertilizers application for a specific area, achieving maximum bread wheat productivity becomes challenging. To address this issue, this field experiment was conducted to evaluate the effect of seed and NP fertilizer rates on growth, yield, and yield components of bread wheat (Triticum aestivum L.). Factorial combinations of four levels of seed rates of bread wheat (100, 120, 150, and 200 kg ha⁻¹) and four levels of NP fertilizers rates (64–46, 87–46, 96–69, and 119–69 kg ha⁻¹ of N and P₂O₅) were laid down in randomized complete block design with three replications. The remaining necessary agronomic practices and crop management activities were undertaken uniformly. The data presented in this dataset article includes phenological, growth, yield, and yield component parameters that were collected timely following their standard methods and procedures. All the collected data were subjected to analysis of variance (ANOVA) which was carried out using the SAS version 9.0 software computer program's General Linear Model (GLM) procedure [1]. the residuals were evaluated, as described in Montgomery [2], to confirm the normal distribution and homogeneous variance model assumptions on the error terms for each response variable. The independence assumption is upheld due to the randomization of the sixteen (16) treatment combinations within each block. In cases where a treatment effect was found to be significant, a multiple means comparison was conducted at a 5% significance level using Fisher's LSD method to create letter groupings. Additionally, correlation analysis was carried out using the Pearson correlation procedure in SAS. This dataset article provides insights into how seed rate and NP fertilizer rates impact bread wheat productivity, as well as the economic viability of optimal seed rate and NP fertilizer rates on bread wheat productivity. The information presented can serve as a valuable resource for researchers looking to examine the supplementary data and methods in detail, potentially leading to new research avenues. Furthermore, it has the potential to foster collaborations and enhance the credibility of the current research data within the scientific community, making it accessible for wider use.

Specifications Table

Subject	Agriculture
Specific subject area	Agronomy
Type of data	Table Analyzed mean data and raw data
Data Collection	Data on bread wheat related to phenological, growth, yield, and yield component parameters were collected by using measurement under field conditions at plant and plot basis based on the nature of the parameter collected.
Description of data collection	Days to 50% emergence, Days to 50% heading, and Days to 90% physiological maturity, Plant height, Number of total tillers and Numbers of effective tillers, Spike length, Number of kernels spike-1, Above ground dry biomass yield, Grain yield, Thousand kernels weight, Straw yield, and Harvest index were obtained as described in this article.
Data source location	Debre Markos University, Burie campus, West Gojjiam, Ethiopia, is the owner of the data presented in this article. The experimental site is located at 10°42’43’’N latitude and 37°4’45’’E longitude with an altitude of 2103 m above sea level [3].
Data accessibility	Repository name: Mendeley Data With article and the raw data are deposited in the Mendeley dataset repository available at: https://data.mendeley.com/datasets/nt5s8ykggr/1 Doi:10.17632/nt5s8ykggr.1
Related research article	Yield response of bread wheat to different rates of seeding and NP fertilizers application in Western Amhara, North Western Ethiopia

1. Value of the Data

• •In Ethiopia, the agro-climatic conditions are conducive to bread wheat production, but the productivity of this crop remains below its potential. This is primarily attributed to various production constraints, with nutrient deficiencies being a significant factor leading to declining soil fertility and inappropriate seed rates exacerbating the issue. To address these challenges and enhance bread wheat productivity, different rates of NP fertilizer have been recommended on a broad scale. However, the current cost of these fertilizers is prohibitive for smallholder farmers, leading to nutrient leaching and further impacting the favorable agro-climatic conditions for bread wheat production in the country. Determining the optimal N and P fertilizer rates is crucial for maximizing economic yields and achieving the full potential of bread wheat production. Therefore, this dataset provides valuable insights into how bread wheat growth, yield, and yield components respond to varying rates of NP fertilizer.
• •The seed rate plays a crucial role in bread wheat production, especially in the context of declining soil fertility in Ethiopia. Optimizing the seed rate can help maximize production potential, improve crop establishment, and mitigate the effects of soil fertility decline. This optimization is essential for ensuring food security, sustaining agricultural productivity and addressing soil degradation challenges in Ethiopia. The information presented in this dataset can assist bread wheat farmers in determining the optimal seed rate for high productivity in their districts and areas with similar agroecology and soil fertility profiles. Agronomists can utilize this dataset to conduct trials on different crops or the same crop in various soil types and seasons, potentially leading to increased crop output at minimal or no cost to farmers and enhancing research efficiency among scholars.
• •Based on the data analysis, researchers can provide practical recommendations for sustainable bread wheat production in Burie District and similar agroecological zones in Ethiopia. These recommendations may include tailored seeding and fertilizer application strategies to optimize crop yields, conserve soil fertility, and promote food security in the region.

2. Background

Bread wheat (Triticum aestivum L.) is a staple crop in Ethiopia, and improving its yield and quality is crucial for meeting the food demands of the growing population. In many agricultural regions, including Burie District, farmers face challenges related to suboptimal seed rates and fertilizer application practices, leading to reduced crop yields and economic losses. Understanding the impact of different seeding rates and NP fertilizer levels on the growth and yield components of bread wheat can provide valuable insights for farmers, researchers, and policymakers to make informed decisions regarding crop management practices.

The motivation behind compiling this dataset was to conduct a field experiment that systematically evaluates the effects of varying seed rates and NP fertilizer levels on the phenological parameters, plant height, tiller development, spike characteristics, biomass yield, and grain yield of bread wheat in the specific agro-climatic conditions of Burie District. By collecting and analyzing comprehensive data on these agronomic parameters, the study aims to generate evidence-based recommendations for optimizing wheat production practices in the region.

Overall, this dataset contributes to the existing knowledge base on wheat cultivation in Ethiopia and provides valuable information that can guide future research efforts, extension services, and agricultural policies aimed at enhancing food security and sustainable crop production in the country.

3. Data Description

This dataset was collected in a field experiment conducted during the main cropping season 2022 rainy season at Burie town of Burie district, Northwestern Ethiopia (Fig. 1). Table 1 illustrates the main effects of Seed rate and NP fertilizer rates on phenological (i.e. Days to 50% emergence, 50% heading, and 90% physiological maturity) parameters of bread wheat in the Burnie district of northwestern Ethiopia. Table 2 highlights the interaction effects of seed rate and NP fertilizer rates on plant height and the number of effective tillers in bread wheat. The main effects of seed rate and NP fertilizer rates on the total number of tillers in bread wheat are presented in Table 3. As shown below in Table 4, spike length, number of kernels per spike, and thousand kernel weight were affected by the main effects of seed rate and NP fertilizer rates. Table 5 of the data also shows the interaction effect of seed rate and NP fertilizer rates on the above-ground biomass yield of bread wheat. Grain yield, straw yield, and harvest index of bread wheat were affected by the main effects of seed rate and NP fertilizer rates as indicated in the analyzed mean data in Table 6. The dataset presented in this article shows that the grain yield of bread wheat was strongly and positively correlated with most of its agronomic parameters as shown in Pearson's correlation (Table 7). The partial budget analyzed dataset for bread wheat production as influenced by seed rate and NP fertilizer rates is also presented in Table 8. This dataset article provides the raw data for parameters collected in the field experiment and thus, the raw data is deposited in the OSFHOME dataset library (https://osf.io/n4eyw/?view_only=1c7887589f6b4566b366d4bfdf5709fd).

Fig. 1.

Map of the study area.

Table 1.

Phenological parameters of bread wheat as affected by the main effect of seed rate and NP fertilizer rates.

Seeding rate (kg ha−1)	DE	DH	DPM
100	4.64	63.08a	110.75a
120	4.76	61.44b	109.58a
150	4.64	61.00bc	107.75b
200	4.68	60.68c	107.33b
Sig. difference	ns	***	***
level of N-P2O5 (kg ha−1)
64–46	4.58	62.53a	107.92b
87–46	4.60	61.76b	108.17b
96–69	4.73	61.31b	109.42a
119–69	4.81	60.61c	109.92a
Sig. difference	ns	***	**
CV (%)	6.54	1.31	1.43
SE ±	0.089	0.24	0.42

** and *** = highly significant and very highly significant respectively; ns= not significant; DE=days to 50% emergence; DH=days to 50% heading; DPM=days to 90% physiological maturity; CV= coefficient of variation; SE ± = standard error.

Table 2.

Interaction effect of seed rate and NP fertilizer rates on plant height and number of effective tillers.

Seeding rate (kg ha−1)	N-P2O5 level (kg ha−1)	PH (cm)	NET/net plot
100	64–46	68.38h	51.00abc
	87–46	72.70gh	58.00ab
	96–69	73.66fg	52.67abc
	119–69	82.98ab	72.67a
120	64–46	76.06defg	50.67abc
	87–46	76.38defg	32.67cd
	96–69	78.86bcde	46.67abcd
	119–69	78.58bcde	72.00a
150	64–46	75.53efg	16.67e
	87–46	77.83cdef	35.00bcd
	96–69	81.40abc	71.33a
	119–69	77.13cdefg	41.00bcd
200	64–46	79.22bcde	31.67cd
	87–46	80.24bcd	33.00cd
	96–69	81.33abc	29.67de
	119–69	85.33a	48.00abcd
Sig. difference		**	*
CV (%)		3.62	9.07
SE ±		1.58	0.076

* = significant; ** = highly significant; PH= plant height; NET= number of effective tillers; CV (%) = coefficient of variation in percent; SE ± = standard error.

Table 3.

The main effects of seed rate and NP fertilizer rates on number of total tillers of bread wheat.

Seeding rate (kg ha−1)	NTT
100	61.00
120	63.58
150	51.00
200	56.67
Sig. difference	ns
Level of N-P2O5 (kg ha−1)
64–46	49.75
87–46	52.67
96–69	66.33
119–69	63.50
Sig. difference	ns
CV (%)	10.50
SE ±	6.87

NTT= number of total tillers; ***= very highly significant; **= highly significant; ns= not significant; CV (%) = coefficient of variation in percent; SE ± = standard error.

Table 4.

The main effects of Seed rate and NP fertilizer rates on spike length (cm), number of kernels per spike and thousand kernels weight (g).

Seed rate (kg ha−1)	SL (cm)	NKPS	TKW
100	7.87a	57.68a	35.77a
120	7.80a	56.72a	34.37b
150	7.70a	54.38ab	34.18b
200	7.45b	50.85b	33.97b
Sig. difference	***	**	*
N-P2O5 level (kg ha−1)
64–46	7.55b	52.67b	33.78b
87–46	7.69ab	53.76b	34.16ab
96–69	7.76a	54.86ab	34.92ab
119–69	7.83a	58.33a	35.42a
Sig. difference	*	*	*
CV (%)	2.77	8.56	4.06
SE ±	0.06	1.25	0.38

***, **, * indicates very highly significant, highly significant and significant respectively; SL= spike length; NKPS= number of kernels per spike; TKW = thousand kernel weight and CV (%) = coefficient of variation in percent; SE ± = standard error.

Table 5.

Interaction effect of seeding and NP fertilizer rates on above ground biomass yield of bread wheat.

Seeding rate (kg ha−1)	level of N-P2O5 (kg ha−1)	AGBY(kg ha−1)
100	64–46	5297.7fg
	87–46	5199.3fg
	96–69	5083.2g
	119–69	5766.8cdef
120	64–46	5567.2efg
	87–46	5627.3defg
	96–69	5406.7efg
	119–69	5460.9efg
150	64–46	5315.7fg
	87–46	5302.1fg
	96–69	6301.3bcd
	119–69	6384.2bc
200	64–46	5261.6fg
	87–46	6088.1bcde
	96–69	6485.9ab
	119–69	7086.8a
Sig. difference		**
CV (%)		7.16
SE ±		210.98

** = highly significant; AGBY= above ground biomass yield; CV (%) = coefficient of variation in percent and SE ± = standard error.

Table 6.

Grain yield, straw yield and harvest index of bread wheat as affected by the main effects of seed and NP fertilizer rates.

Seeding rate (kg ha−1)	GY (kg ha−1)	SY (kg ha−1)	HI (%)
100	2674.30b	2662.4b	50.53
120	2816.30b	2715.70b	51.09
150	3125.00a	2700.80b	54.03
200	2889.80ab	3341.00a	46.62
Sig. difference	*	**	ns
N-P2O5 level (kg ha−1)
64–46	2675.30b	2685.20b	50.25
87–46	2856.50ab	2697.70b	51.75
96–69	3116.40a	2703.00b	53.73
119–69	2857.20ab	3334.00a	46.55
Sig. difference	*	**	ns
CV (%)	11.95	17.05	12.64
SE ±	97.39	163.46	2.07

**, * highly significant and significant respectively; ns = not significant; GY= grain yield; SY= straw yield; HI (%) = harvest index in percent; CV (%) = coefficient of variation in percent and SE ± = standard error.

Table 7.

Simple correlation analysis among bread wheat agronomic parameters.

	DH	DPM	PH	NET	SL	NKPS	TKW	AGBY	GY	SY
DH	1
DPM	0.203ns	1
PH	−0.650***	−0.090ns	1
NET	0.025ns	0.590***	0.108ns	1
SL	0.147ns	0.252ns	−0.167ns	0.315*	1
NKPS	0.059ns	0.558***	−0.104ns	0.611***	0.313*	1
TKW	0.039ns	0.392**	0.022ns	0.361*	0.312*	0.167ns	1
AGBY	−0.505***	−0.280ns	0.482***	0.038ns	−0.075ns	0.117ns	−0.139ns	1
GY	−0.469***	−0.128ns	0.244ns	0.098ns	0.063ns	−0.052ns	−0.014ns	0.346*	1
SY	−0.226ns	−0.200ns	0.340*	−0.020ns	−0.107ns	0.160ns	−0.147ns	0.806***	−0.274ns	1

DH=days to 50% heading; DPM=days to 90% physiological maturity; PH = plant height; NET = number of effective tillers; SL= spike length; NKPS= number of kernels per spike; TKW = thousand kernel weight; AGBY= above ground biomass yield; GY= grain yield; SY= straw yield; ns= not significant; * = significant; **= highly significant; ***= very highly significant.

Table 8.

Partial budget analysis for bread wheat yield as influenced by seed rate and NP fertilizer rates for bread wheat crop production.

Treatment	UAY (kg ha−1)		AGY (kg ha−1)		Total sale price (Birr ha−1)		TGB (Birr ha−1)	TVC (Birr ha−1)	NB (Birr ha−1)	MRR (%)
	GY	SY	GY	SY	GY	SY
T1 (SR1F1)	2526	2771	2273	2494	23,870.70	997.56	24,868.26	3850.00	21,018.26	–
T5 (SR2F1)	2779	2816	2501	2534	26,261.55	1013.76	27,275.31	4170.00	23,105.31	652.20
T2 (SR1F2)	2630	2569	2367	2312	24,853.50	924.84	25,778.34	4375.00	21,403.34	D
T9 (SR3F1)	2919	2397	2627	2157	27,584.55	862.92	28,447.47	4650.00	23,797.47	144.20
T6 (SR2F2)	3093	2535	2784	2282	29,228.85	912.60	30,141.45	4695.00	25,446.45	3664.40
T3 (SR1F3)	2766	2317	2489	2085	26,138.70	834.12	26,972.82	4975.00	21,997.82	D
T10 (SR3F2)	2888	2415	2599	2174	27,291.60	869.40	28,161.00	5175.00	22,986.00	D
T7 (SR2F3)	2846	2561	2561	2305	26,894.70	921.96	27,816.66	5295.00	22,521.66	D
T13 (SR4F1)	2477	2784	2229	2506	23,407.65	1002.24	24,409.89	5450.00	18,959.89	D
T4 (SR1F4)	2774	2993	2497	2694	26,214.30	1077.48	27,291.78	5500.00	21,791.78	D
T11 (SR3F3)	3693	2608	3324	2347	34,898.85	938.88	35,837.73	5775.00	30,062.73	427.43
T8 (SR2F4)	2548	2979	2293	2681	24,078.60	1072.44	25,151.04	5820.00	19,331.04	D
T14 (SR4F2)	2816	3272	2534	2945	26,611.20	1177.92	27,789.12	5975.00	21,814.12	D
T12 (SR3F4)	3000	3384	2700	3046	28,350.00	1218.24	29,568.24	6300.00	23,268.24	D
T15 (SR4F3)	3160	3327	2844	2994	29,862.00	1197.72	31,059.72	6575.00	24,484.72	D
T16 (SR4F4)	3106	3980	2795	3582	29,351.70	1432.80	30,784.50	7100.00	23,684.50	D

UGY= unadjusted yield; AGY= adjusted yield; GY= grain yield; SY= straw yield; GB= gross benefit; TVC= total variable cost; NB= net benefit; SR1= 100 kg ha⁻¹; SR2=120 kg ha⁻¹; SR3= 150 kg ha⁻¹; SR4= 200 kg ha⁻¹; F1= 64–46 NP kg ha⁻¹; F2=87–46 NP kg ha⁻¹; F3= 96–69 NP kg ha⁻¹; F4= 119–69 NP kg ha⁻¹.

Note: cost of wheat seed, DAP, Urea and wheat straw per kilogram was 10.50, 12.00, 10.50 and 0.40 Birr based on the local market.

4. Experimental Design, Materials and Methods

Factorial combinations of four levels of seed rate (100, 120, 150, and 200 kg ha⁻¹) and four levels of NP fertilizer rate (64–46, 87–46, 96–69, and 119–69 kg ha⁻¹) were laid out in RCBD1 with three replications. The land was prepared by oxen/animal-drawn plowing and hand tools, and after that leveled and smoothed by human labor using hand tools. As per the design and treatments, the experimental field was then manually subdivided into blocks and plots based on the design and treatments. An experiment had a total area of 9.2 m width × 45.9 m length (422.28m²) and a gross plot size of 2.4 m width x 2.4 m length (5.76m²). A 1.0 m wide open path separated the adjacent blocks and a 0.5 m wide path separated plots within a block from one another. The required seeding rates for experimental plots were determined as per treatments and seeds were drilled in the open rows which were 20 cm apart from one to immediate other. Using a random table number, experimental treatments were assigned to experimental plots in each block. As a result, there were 12 rows totaling 2.4 m in length. To avoid possible border effects, the net plot size (harvestable area) was 1.2 m x 0.5 m (0.6m²) (i.e. the middle 6 rows from each plot) by excluding 3 outermost rows on both sides of each plot horizontally and 0.95 m row segment from both ends of the plot vertically. Then, all the remaining necessary agronomic practices and crop management activities (such as plot preparation, sowing, weeding, harvesting, and threshing) were undertaken uniformly.

4.1. Crop data collected

4.1.1. Phenological parameters

Days to 50% emergence were recorded from the time of sowing to the date when 50% of the plants appeared above the ground from each plot. Days to 50% heading were recorded visually when the panicles of 50% of the plants from each net plot area were fully visible or produced spikes above the sheath of the flag leaf. Days to 90% physiological maturity were recorded by counting the number of days from the date of sowing until 90% of the plants in the net plot area changed from green to yellowish color.

4.1.2. Growth parameters

The plant height of bread wheat was measured in centimeters as the average height of ten randomly selected plants from the net plot area of each plot from the ground to the top of the spike, excluding awns at maturity. The total number of plants before and after tillering was counted in each net plot. Then, the number of total tillers was calculated by subtracting the value of the total number of plants before tillering from the total number of plants after tillering. The numbers of effective tillers per net plot were counted with a similar technique applied in counting the total number of tillers at the physiological maturity period.

4.1.3. Yield and yield component parameters

The average spike length of ten randomly selected plants from the net plot area of each plot was measured in centimeters from the base to the uppermost part of the spike excluding awns at maturity. The number of kernels per spike was counted from ten randomly selected plants from each net plot and the mean kernel number was taken at harvesting. The sun-dried total above-ground plant biomass yield (straw + grain) from the net plot area of each plot at the time of harvesting was measured. The result was converted into a hectare basis. Grain yield was determined from the net harvestable area; the yield was adjusted to 12.5% moisture content, and the result was converted to kg ha⁻¹ [4].

$$\text{Grain}\text{yield}\left(\text{kg}\mathrm{h}{\mathrm{a}}^{-1}\right)\text{at}12.5\%\text{moisture}\text{base}=\text{Obtained}\text{yield}\left(\text{kg}\mathrm{h}{\mathrm{a}}^{-1}\right)\times \frac{100-\%\text{MC}}{100-12.5}$$ (1)

Where, MC= grain moisture content. Thousand kernel weights were determined based on the weight of thousand kernels taken from the sample used to determine the grain yield of each treatment counted manually. An electronic balance was used for weighing after adjusting to a 12.5% moisture level. The straw yield was measured by taking the weight of the straw harvested from the net plot area of each plot and converted to kilograms per hectare after sun drying the straw. Harvest index (HI): It was calculated as the ratio of grain yield to total above-ground dry biomass yield multiplied by 100 at harvest from the respective treatments [5].

$$\text{HI}(\%)=\frac{\text{Grain}\text{yield}}{\text{Aboveground}\text{dry}\text{biomass}\text{yield}}\mathrm{X}100$$ (2)

4.2. Statistical data analysis

The collected data were subjected to analysis of variance (ANOVA) and the analysis was carried out using the SAS version 9.0 software computer program's General Linear Model (GLM) procedure [1]. As described in Montgomery [2], the residuals were examined to verify the normal distribution and homogeneous variance model assumptions on the error terms for each response variable. Because the twelve treatment combinations were randomized within each block, the independence assumption is valid. When a treatment effect was significant, multiple means comparison was performed at a 5% level of significance using the least significant difference (Fisher's LSD) method to generate letter groupings, and correlation analysis was performed using the Pearson correlation procedure found in SAS.

4.3. Partial budget analysis

A partial budget analysis was conducted to assess the economic viability of the treatments involving seed rate and NP fertilizer rates. The analysis included partial budget, dominance, and marginal analyses. The partial budget and marginal analysis methods were utilized to evaluate the economic data. Partial budgeting involves organizing experimental data and information regarding the costs and benefits of different treatment options. Marginal analysis is a technique used to compare costs that vary with the net benefits to determine the most suitable technology for recommendations based on the experiment. Market prices of bread wheat in Ethiopian Birr per kilogram were obtained from local markets during harvest, while variable costs for NP fertilizer, transportation, and application costs for both seed rate and NP fertilizer were considered during sowing.

The partial budget averaged of the sixteen (16) treatments calculated from income and expenses based on variable cost (Table 8). The mean yield was adjusted downward by 10% and was used to reflect the difference between the experimental field and the expected yield from farmers’ fields with farmers’ practices from the same treatments [6]. The gross field benefit was calculated by multiplying the adjusted grain yield of each treatment with the farm price of the crop during harvesting. All variable costs were calculated excluding the price of other agronomic practices such as land plowing, sowing, weeding, protection of the farm, and harvesting because it was uniform for all treatments. The variable costs were summed up and subtracted from gross field benefits which were taken as net benefits. Based on the results of the dominance analysis, treatments were chosen in increasing order of total variable costs. For each pair of ranked treatments, the percent marginal rate of return (MRR) was calculated. The MRR (%) between any pair of un-dominated treatments was the return per unit of investment in fertilizer. It was calculated by dividing the change in net benefit by the change in variable costs. Analysis of marginal rate of return (MRR) was carried out for non-dominated treatments and the MRRs were compared to a minimum acceptable rate of return (MARR) of 100% to select the optimum treatment [7].

Limitations

The data presented in the article “The effect of seeding and nitrogen-phosphorous fertilizer rates on growth and yield components of bread wheat (Triticum aestivum L.) in Burie District, Northwestern Ethiopia: Dataset article” may have several limitations that should be considered. Firstly, the study's sample size may have been relatively small, which could restrict the generalizability of the findings to a larger population or different agricultural settings. Additionally, the duration of the study may have been limited, potentially overlooking the long-term effects of the various treatments on wheat growth and yield. Furthermore, the results may be specific to the conditions in Burie District, Northwestern Ethiopia, and may not be easily extrapolated to other regions with distinct soil types, climate conditions, or farming practices.

Ethical Statements

The dataset collected in this study did not involve animals and humans.

CRediT authorship contribution statement

Kelemu Nakachew: Conceptualization, Methodology, Software, Writing – original draft, Investigation, Formal analysis. Fenta Assefa: Conceptualization, Methodology, Writing – original draft, Investigation, Formal analysis, Writing – review & editing. Habtamu Yigermal: Supervision, Writing – review & editing.

Data Availability

• The Effect of Seeding and NP fertilizer Rates on Growth and Yield Components of Bread Wheat (Triticum aestivum L.) in Burie District, Northwestern Ethiopia: Dataset Article (Original data) (Mendeley Data)