Research Article | | Peer-Reviewed

Effect of Various Improved Technology in Wheat Production Zone of Nepal

Received: 11 May 2024     Accepted: 27 May 2024     Published: 6 June 2024
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Abstract

The study was held in 2024 in the Kailari, Gauriganga, Godawari, and Dhangadhi local level of Kailali District of Nepal to evaluate and identify the factors impacting the adoption of a number of sophisticated technologies in the research area. Data were collected from 200 respondents utilizing a semi-structured interview form, using simple random selection. The factors influencing the adoption of better technologies in wheat production were identified using a logistic regression model. Age, gender, ethnicity, and area of cultivation are socioeconomic elements that have been linked to the adoption of appropriate agricultural practices, as well as training, technical advice, and membership. The adoption of seed replacement was positively significant (P<0.1) as a result of the training. The adoption of seed varieties was positively significant (P<0.05) for cultivated area. The farmers who were involved in farmer groups or Cooperative had 2.209 times higher odds for the adoption of improved seed compared to the odds for farmers who were not involved in farmer groups. Advice from the technician had a positively significant (P<0.05) impact on the date of sowing. The use of more frequent irrigation was positively significant (P<0.05) in relation to the age of the household head. The split nitrogen application was positively significantly influenced by super zone membership (P<0.05).

Published in International Journal of Applied Agricultural Sciences (Volume 10, Issue 3)
DOI 10.11648/j.ijaas.20241003.15
Page(s) 126-137
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Adoption, Innovation, Technology, Wheat, Super-Zone

1. Introduction
Farmers adopt innovations after they encounter, consider, and eventually reject or practice them . Aggregate adoption across all farms will be a gradual process. An individual may stop using innovation due to personal, institutional, or social reasons, such as finding an idea or practice more suitable for their needs .
It has been assumed that the cumulative rate of adoption of agricultural technology over time follows an S-shaped logistic function with a slow start, followed by a progressive adoption phase, and finally a convergence towards the maximum level asymptotic to the maximum level . Ban and Hawkins found that innovation adoption patterns differed by crop type, location, and innovation type.
This crop is the most widely cultivated cereal crop in the world. According to Sharma . wheat is cultivated on 21% of land and accounts for 17% of total cereal production in the world. For agriculture commercialization and mechanization in the country, the MoALD has proposed pockets, blocks, zones and super-zones to address fragmented arable land . Wheat super-zone not as it were assists in mechanization and commercialization, but it also provides a few specialized assist to the farmers through its technicians and specialists. It moreover points to upgrade the livelihood and economy of the locale through a change in wheat cultivation and production technology .
Nepalese wheat productivity is still in subsistence level. So, for the increased farm production and productivity, adoption of new suitable farming practices is the fundamental need for the country today. However, the process of dissemination of improved farming technology is being hampered severely by various obstacles. Thus, this study was deemed necessary and was undertaken. This study attempts to determine the factors associated with improved wheat cultivation adoption by beneficiaries of super-zone compared to non-beneficiaries.
2. Methodology
2.1. Site of Study
The Kailali district was selected as a super zone for wheat production established under Prime Minister Agriculture Modernization Project (PMAMP).
Figure 1. Map of Nepal showing research sites.
2.2. Sample Size
The size of the sample, and amount of variation, usually affect the quantity and quality of information obtained from the survey. Utilizing suitable inspecting methods, both factors can be controlled .
Kinnear and Tayler suggested that a good survey sample should have both a small sampling error and minimum standard error. The minimal sample size for a bigger population that offers a suitable level of assurance for decision-making is typically thought to be 60 .
Taking 50 growers each from Kailari, Gaurigangar, Godawari and Dhamgadhi. 100 samples were from household under the membership of PM-AMP (Kailari and Gauriganga) while remaining samples were from non-member households.
2.3. Sample Selection Procedure
During the process of sample selection simple random sampling is the best way to avoid bias in which each unit of the population has an equal chance for selection .
Thus by using sampling frame, a simple random sampling procedure was used to collect necessary information from wheat growers. The procedure was comprehensive and representative of the whole population.
2.4. Methods of Data Collection
Household survey is used for collection of necessary information. In this study, both the primary and secondary data were collected. The methodologies consisted of field survey, review of previous studies, and interviews with key informants.
2.5. Techniques of Data Collection
Primary data were gathered through a schedule of interviews. A variety of facts about wheat production were gathered. Face-to-face interviews were used to gather information about the features of the farm and home, as well as production and management factors.
2.6. Pre-testing of Interview Schedule
The major goals of this activity are to organize a fieldwork plan, assess the reliability of the questionnaire, and estimate various cost components such as financial costs, travel costs, interview time, etc. before the main survey. The interview schedule was pre-tested in nearby villages with 10 farmers from Mahara village before the questionnaire was given to the actual respondents. The schedule was amended where necessary, edited, and given its final shape. Methods and techniques of data analysis.
After collection of required data, it was coded and entered into a computer for analysis. Statistical programs for the Social Sciences (SPSS) and Stata were used to input the data and conduct the analysis. For multiple regression, the required inference was derived using the mean, standard deviations, frequency, percentage, and Ordinary Least Square Technique.
2.7. Quantitative Data Analysis
We used both descriptive and analytical statistics to analyze quantitative data. Simple descriptive statistics like frequency count, percentage, mean, standard deviation, etc. were used to describe the respondents' socioeconomic and farm characteristics such as family size, age, gender, occupational pattern, land holding size, and population of economically active people.
2.8. The Logit Model
It is employed in the discrete model, which yields outcomes similar to those of the probit model . It is a multivariate statistical method that enables the prediction of dichotomous dependent variables from dependent variables .
Hosmor and Lemshew noted that a logistic distribution (logit) has an advantage over the others in the study of dichotomous outcome variables since it is a flexible and simple model to utilize from a mathematical perspective and produces an insightful interpretation.
Logistic equation is given by;
p/(1-p)=eb0+b1x1+b2x2+…bnxn
Where, p/(1-p) is odds of an event
p is the probability
e is base of natural logarithm
b0…bn are coefficients
x1…….xn are independent variables.
Logit form of equation can be obtained by taking natural log both sides,
ln(p/1-p)=b0+b1X1+b2X2+…….bnXn
Table 1. Description of variables used in the logistic regression model.

Variables

Description of variables

Types of variables

Unit

Dependent variables in Logistic regression:

Split nitrogen

Split dose of nitrogen apply by farmers

Dummy

(1- two time, 0- one time)

Crop residual

Crop residual management by farmers

Dummy

(1- incorporate in soil, 0-stubble burning)

Irrigation Frequencies

Number of irrigation applied by farmer

Dummy

(1- two times, 0- single time)

Date of sowing

Appropriate date of sowing of wheat by farmers

Dummy

(1- November 10, 0- otherwise)

Seed variety

Seed variety used by farmers

Dummy

(1-Improved, 0- local)

Seed replacement

time of seed replacement by farmers

Dummy

(1- within 3 years, 0- otherwise)

Independent variables in Logistic regression:

Age

Age of household head

Continuous

Years

Amount of land

Land amount used for wheat production

Continuous

kattha

Purpose of crop

Purpose of wheat grain by farmers

Dummy

(1- Commercial, 0- home consumption)

Gender

Gender of household head

Dummy

(1- male, 0- female)

Ethnicity

Ethnicity of household head

Dummy

(1- janajati, 0- otherwise)

Training

Training on wheat cultivation

Dummy

(1- Yes, 0- No)

Advice from technicians

Advice from technicians for wheat production

Dummy

(1- Yes, 0- No)

Super zone

Respondents under the super zone

Dummy

(1- member, 0- non member)

Involvement of extension worker

Involvement of extension worker for provision of information

Dummy

(1- Yes, 0- No)

Involvement in group

Involvement of respondent in farmer group

Dummy

(1- Yes, 0- No)

Active population

Active age group in family member

Dummy

(1- Yes, 0- No)

Personal contact to extension worker

Personal contact of respondents to extension worker

Dummy

(1- Yes, 0- No)

Land rented in

Land rented in by respondent

Continuous

Kattha

Occupation

Occupation of household head

Dummy

(1- Agriculture, 0-otherwise)

Year of schooling

Year of schooling of household head

Continuous

Years

Source: Authors illustrations.
3. Results
3.1. Package of Practices/Technologies
3.1.1. Seed Replacement
The majority of farmers replaced their wheat seeds within three years, according to table 2. Farmers were not replenishing seeds on average in 19% of cases. Farmers who didn't replace their seeds accounted for 12% of the total in the super zone, compared to 7% in the non-super zone.
Table 2. Status of seed replacement in study area.

Seed replacement

Total (n=200)

Super zone (n=100)

Non-super zone (n=100)

No change (Yes)

19 (9.5)

12 (12)

7 (7)

Change within 3 years (Yes)

181 (90.5)

88 (88)

93 (93)

Note: Figures in parentheses indicate percent.
Source: Field Survey, 2024.
3.1.2. Variety Used
The acceptance of the enhanced variety was deemed adequate. A little over 83% of the farmers utilized enhanced wheat varieties. According to Table 3, there were 84 percent of such farmers in the super zone and 82 percent in the non-super zone, respectively.
Table 3. Status of seed variety used at study area.

Variety

Total (n=200)

Super zone (n=100)

Non-super zone (n=100)

Chi-square value

Local

34 (17)

16 (16)

18 (18)

0.142

Improved

166 (83)

84 (84)

82 (82)

Note: Figures in parentheses indicate percent.
Source: Field Survey, 2024.
3.1.3. Time of Irrigation Application
Nearly 80% of the respondents applied irrigation at the Crown Root Initiation (CRI) stage, it was discovered. Only 1% of all respondents were discovered applying irrigation at the node creation stage, compared to around 3.5% of all respondents applying irrigation at the tillering stage. Similar to this, only 1 out of 200 respondents administered irrigation at the milking stage, but 4.5% of all respondents did so at the blossoming stage.
Table 4. Irrigation status in the study area.

Irrigation (Yes)

Total (n=200)

Super zone (n=100)

Non-super zone (n=100)

Chi-square value

CRI

198 (99)

99 (99)

99 (99)

Tillering

7 (3.5)

6 (6)

1 (1)

3.701*

Node formation

2 (1)

0 (0)

2 (2)

2.020

Flowering

9 (4.5)

4 (4)

5 (5)

0.116

Milking

1 (0.5)

1 (1)

0 (0)

1.005

Grain filling

0 (0)

0 (0)

0 (0)

Notes: Figures in parentheses indicate percent, * indicates significant difference at 10 percent level.
Source: Field Survey, 2024
3.1.4. Weeding
In the study area, it was shown that just 43% of the households really weeded their wheat fields. It was discovered that 50% of households in super zones and 36% of households in non-super zones practiced weeding, and it was determined that there was a substantially distinct pattern at the 5% level of significance, as shown in Table 5.
Table 5. Status and methods of weeding in the study areas.

Variables

Total (n=200)

Super zone (n=100)

Non-super zone (n=100)

Chi-square value

Weeding (Yes)

86 (43)

50 (50)

36 (36)

3.998**

Method of weeding

8.282***

a. Manual

52 (59.8)

24 (47.1)

28 (77.8)

b. Chemical

35 (40.2)

27 (52.9)

8 (22.2)

Notes: Figures in parentheses indicate percent. ***and ** indicate 1percent, and 5percent levels of significance, respectively.
Source: Field Survey, 2024
3.1.5. Insect Pest and Disease Management
The measures used by respondents to handle insects and diseases in their wheat fields are shown in Table 6. In the research area, just 11% of households used insect pest management techniques. When compared to non-super zones, the percentage of such homes in the super zone was much higher (18%). (4 percent). Only 9% of respondents were found to have controlled the illness in wheat fields. Only 4% of non-super zone households and 14% of super zone households undertook illness management. At a 5% level of significance, the difference was determined to be statistically significant.
Table 6. Insect pest and Disease management system at the study area.

Insect and disease management

Total (n=200)

Super zone (n=100)

Non-super zone (n=100)

Chi-square value

Insect management (Yes)

22 (11)

18 (18.0)

4 (4)

10.010***

Methods of insect management

1.086

Cultural (Yes)

2 (9.1)

2 (11.1)

0 (0)

Biological (Yes)

2 (9.1)

2 (11.1)

0 (0)

Chemical (Yes)

18 (81.8)

14 (77.8)

4 (100)

Disease Management (Yes)

18 (9)

14 (14)

4 (4)

6.105**

Methods of disease Management

1.029

Cultural (Yes)

1 (5.6)

1 (7.1)

0 (0)

Biological (Yes)

2 (11.1)

2 (14.3)

0 (0)

Chemical (Yes)

15 (83.3)

11 (78.6)

4 (100)

Note: Figures in parentheses percent. **, *** indicates significant difference at 5 percent and 1percent level of significance.
Source: Field Survey, 2024
3.1.6. Residual Management
It was discovered from the survey area that 99.5% of all respondents were involved in residual management in wheat fields. 79% of the households burned their stubble, compared to 21% who integrated wastes into the soil. In the super zone and non-super zone, respectively, homes that had absorbed leftovers into the soil made up 28% and 14% of all households. As demonstrated in Table 7, the difference was substantial at a 5 percent level.
Table 7. Residual management status of respondents in the study area.

Residual management

Total (n=200)

Super zone (n=100)

Non-super zone (n=100)

Chi-square value

Residual Management (Yes)

199 (99.5)

100

99 (99)

1.005

Method of residual management

5.907**

Incorporate in soil

42 (21)

28 (28)

14 (14)

Stubble burning

158 (79)

72 (72)

86 (86)

Note: Figures in parentheses indicates percent. * *indicates significant different at 5 percent level.
Source: Field Survey, 2024
3.1.7. Nitrogen Application
12.5 percent of households just used nitrogen when preparing the land. The remaining 87.5 percent of families used split dosages of nitrogen, top dressing one month after sowing and basal during field preparation. In super zones, 92 percent of families applied split doses, compared to 83 percent in non-super zones (Table 8).
Table 8. Nitrogen application in the study area.

Nitrogen application

Total (n=200)

Super zone (n=100)

Non-super zone (n=100)

Chi-square value

Nitrogen Doses

3.703*

a. one time

25 (12.5)

8 (8)

17 (17)

b. two time

175 (87.5)

92 (92)

83 (83)

Note: Figures in parentheses indicates percent. * indicates significant different at 10 percent level.
Source: Field Survey, 2024
3.2. Factors Affecting Adoption of Good Agriculture Practices
3.2.1. Factors Affecting the of Split Nitrogen Application
According to the findings of a binary logistic regression analysis of the factors influencing the application of split nitrogen, five of the eight explanatory variables—including the household head's age, membership in a super zone, advice from a technician, training, and gender—were found to be significant at the 1%, 5%, or 10% level of significance.
According to the study, split nitrogen application was negatively and significantly impacted by household head age (p 0.01). It means that the probability of split nitrogen application were 0.93 times higher if the age of the household head was raised by one year. The calculation of the marginal effect revealed that a one-year increase in household head age resulted in an average 0.6% drop in adoption likelihood. Similar to this, super zone membership had an effect on the split nitrogen application that was highly substantial (P 0.05). It means that the probabilities of adopting split nitrogen for a member of a super zone are 3.56 times higher than for a non-member of a super zone. The probabilities of adoption were 4.38 times higher for farmers who had taken part in wheat farming instruction than they were for farmers who had not. The technician's advice had a positive, statistically significant (P 0.1), influence on the split nitrogen application. With odds ratio of 3.785, gender (male) had a positive significant (P0.1) impact on the split nitrogen application, while odds for farmers who routinely sought technical assistance were 4.40 times higher than odds for farmers who did not.
3.2.2. Factors Affecting the Crop Residual Management
The outcome displays the binary logistic regression analysis of the variables influencing the management of crop residuals. As recommended by conservation agriculture, stubble assimilation in soil is considered to be beneficial practice. The odds ratio results indicate that three factors—Super Zone, Amount of Land, and Ethnicity—were significant at a 1 percent and 5 percent level for each of the six explanatory variables. According to the findings, super zone farmers had a 3.96-times greater chance of incorporating stubble than non-super zone farmers did. The amount of land had a substantial favorable (P 0.01) impact on how crop residues were managed. This indicates that the likelihood of adopting stubble inclusion as a crop residue management strategy increased by 1.04 times for every kattha of additional land. The management of agricultural residue was favorably significant (P 0.05) in relation to ethnicity. This indicates that the likelihood of adopting stubble for Janajatis was 3.575 times higher than the likelihood of adopting stubble for non-Janajatis.
3.2.3. Factors Affecting Adoption of Practice of Irrigating Twice
Table 9 displays the findings of a binary logistic regression study of the variables influencing the adoption of twice-daily irrigation. According to odds ratio results, two variables—household head's age and the crop's intended use—were shown to be significant at 5% and 10% levels of significance among nine different explanatory variables. The study found that the household head's age was positively significant (P 0.05) in relation to applying irrigation more frequently. With each additional year of household head age, the likelihood of using irrigation twice increased by 1.047 times. The decision to use irrigation more frequently was favorably significant (P 0.1) in relation to the goal of crop production. The outcome demonstrates that the probability of twice irrigation for a farmer cultivating for sale is 1.67 times greater than the probability of twice irrigation for a farmer cultivating for consumption.
3.2.4. Factors Affecting the Adoption of Date of Sowing
Table 9 displays the findings of the binary logistic regression analysis of the variables influencing the choice of sowing date. According to the odds ratio results, two variables—Super Zone and Advice from technician—had significant results at the 1% and 5% levels of significance among the twelve different explanatory factors. According to the study, Super Zone was favorably significant (P 0.01) on the sowing date. For farmers in super zones, the chances of sowing at the right time are 5.685 times higher than for farmers in non-super zones. The odds ratio for the impact of the technician's advice on the date of sowing was 9.035, which was favorably significant (P 0.05).
3.2.5. Factors Affecting the Adoption of Improved Seed Variety
The outcome of the binary logistic analysis of the variables influencing the adoption of Seed variety is shown in Table 9. According to the odds ratio results, two factors—Land and Involvement in the Farmer Group—had significant results at the 5% and 10% levels of significance among the seven explanatory variables. The study found that the adoption of seed variety was favorably significant (P 0.05) for land. It implies that the likelihood of using better seed increased by 3.502 times for every unit increase in the area of wheat that is cultivated. Participation in farmer groups positively significantly affected the adoption of better seed varieties (P 0.1). When compared to farmers who weren't active in farmer organizations, farmers who were in groups had probabilities of adopting enhanced seed that were 2.209 times greater.
3.2.6. Factors Affecting the Adoption of Seed Replacement
The findings from a binary logistic regression analysis of the variables influencing the adoption of seed replacement are presented in Table 9. According to the odds ratio results, two variables—ethnicity and training—were identified as significant at the 1% and 10% levels of significance among the six explanatory variables. The study found that ethnicity had a substantial detrimental impact on the adoption of seed replacement (p 0.01). According to the findings, Janajati farmers had a 0.41-times greater chance of adopting seed replacement within three years than non-Janajati farmers. The adoption of seed replacement was positively significantly impacted by the training (P 0.1). The findings indicate that compared to farmers who did not receive any training, those who did had 2.77 times the likelihood of adopting seed replacement within three years.
Table 9. Factors affecting adoption of different packages of practices for wheat production.

Variables

Odds Ratio

Split nitrogen

Residual management

Frequencies of irrigation

Date of sowing

Improved seed variety

Seed replacement

Age

0.933***

0.979

1.047**

0.978

0.995

1.004

Amount of land

1.037

1.036***

0.948

0.989

3.502**

Purpose of crop

1.569

1.666*

0.772

1.326

Gender

3.785*

0.807

Ethnicity

0.801

3.575**

0.401

0.411***

Training

4.386*

1.731

3.784

2.775*

Advice from technicians

4.404*

9.035**

0.987

Super zone

3.556**

3.963***

2.406

5.685***

1.100

0.755

Involvement of extension worker

0.950

Involvement in group

1.412

1.047

2.209*

1.605

Active population

1.031

1.192

Personal contact to extension worker

2.129

Land rented in

1.038

Occupation

3.978

Year of schooling

1.043

Summary Statistics

Number of observation

200

200

200

200

200

200

Log Likelihood

-59.056

-85.91

-50.314

-54.04

-82.11

-128.67

LR Chi2

32.60***

33.76***

15.70*

21.95**

18.12**

11.85*

Prob>Chi2

0.000

0.000

0.073

0.024

0.011

0.065

Pseudo R2

0.216

0.164

0.137

0.168

0.099

0.044

Note: ***, ** and * indicate significant at 1percent, 5percent and 10percent levels, respectively.
Source: Field survey 2024.
4. Discussion
4.1. Packages of Practices/Technologies in Wheat
4.1.1. Seed Characteristics
The largest amount of wheat could be produced in Ethiopia from a seed rate of 125 kg, and the least amount could be produced from a seed rate of 200 kg . Although 100 kg/ha of seed generated the most grains per spike, it was discovered that 160 kg/ha of seed produced the maximum grain yield .
Quality seed is thought to be the most fundamental, important, and affordable input for increasing productivity (Rana, 1997). Presently, there are two major constraints for seed system development in Nepal – (i) limited choices of wider range of preferred varieties available to farmers and (ii) easy access and availability of research developed varieties to farmers at right time and right place in affordable prices. High adoption lags of varieties in farmers' fields due to inefficient seed systems' inability to supply seeds quickly at the farm level are the main obstacles and problems in research.
And support services for effective operation of Nepal's present seed system . According to Harris et al. (2007), seed priming improved maize establishment, growth, and flowering as well as increased seed tolerance to unfavorable environmental conditions and increased yield. The typical priming period for wheat seeds was 12 hours, which improved germination, plant growth and development, and yield . Ethiopian wheat growers should adopt the good practice of replacing their seeds every year to boost grain yield .
4.1.2. Land Management
The no-till method produced considerably more organic matter overall . In Ethiopia, conservation agriculture and agroforestry are effective wheat adaptation techniques that raise wheat yield . Repeated tillage (conventional) is thought to promote water and air flow, boost root growth, speed up germination, and lessen the chance of crop loss during an early rainy season .
4.1.3. Sowing Method
Following seed broadcasting and seed broadcasting in standing water, the drilling method of sowing had a beneficial impact on plant height, number of tillers per plant, number of spikes per plant, and number of grains per spike . According to studies done in Ethiopia, row planting yielded more wheat than sowing using the spread approach .
It was determined that wheat sowing under bed planting produced better results, with the maximum plant height, number of tillers, number of grains per spike, 1000 grain weight, grain yield, and water productivity, whereas these parameters were seen as the lowest under broadcasting. Compared to wheat that was broadcast, wheat grown on beds yielded 13% more while using 35% less water .
4.1.4. Depth and Spacing
For irrigated normal seeded conditions, the row spacing should be 22–23 cm, whereas it should be 15–18 cm for late sown conditions. The most fertile soil zone should be sown at a depth of 5 cm . In Nepal, the standard row-to-row spacing for wheat is 22 cm, and the depth of sowing is 5 to 6 cm .
4.1.5. Irrigation
When compared to other moisture levels and no irrigation, three irrigations administered at 25, 40, and 55 days after sowing (DAS) were the most successful . The primary factor increasing wheat yield and productivity is irrigation, and enough irrigation increases production. In Shreepur VDC, Kanchanpur district of Nepal, irrigation enhanced wheat production by almost 193 percent .
If there is only one irrigation available, use it at the CRI stage, which is 20–25 days after sowing. Every week when the first irrigation for the CRI stage is delayed, it has been discovered, the yield is reduced by 2-4 quintals per hectare .
4.1.6. Weeding
According to the study, weed infestation in wheat fields lowered yield by roughly 25.35 percent . Although chemical methods appear to be profitable, manual and biological methods are found to be more environmentally friendly and improve the health of the soil. The chemical method of weed control was found to be more effective in weed control and per unit production of wheat was also higher than other methods of weed control .
4.1.7. Disease and Insect Pest Management
Rusts, blotches, and head blight/scab are prominent wheat diseases that now contribute to these losses. Wheat blast and spot blotch, two more recently discovered or comparatively unknown diseases, also pose a threat to grain output. The production of wheat is significantly hampered by pathogenic fungus. Since the crop was domesticated, rust infections have hampered global wheat production and continue to pose a threat to the global wheat supply . Fluconazole, Tebuconazole, or Epoxyconazole + Carbendazim applied to plants twice during Zadoks development stages 31 and 45 resulted in disease severity reductions of 96.3 percent, 93.9 percent, and 91 percent, respectively . According to , the annual average of real yield losses brought on by all wheat illnesses in both industrialized and developing nations was around 12.4%. Pucciniatriticina-caused leaf rust was by far the most significant disease, resulting in average yearly losses of 3.48 percent, followed by the wheat streak mosaic virus (1.88 percent) and the Septoria complex (1.6 percent) . In general, disease-related yield loss in wheat production should not exceed 0.1 to 2 percent . Insects that feed on wheat and chew it typically do not cause significant direct damage until population numbers are quite high .
4.1.8. Use of Fertilizers
In comparison to a wheat crop that was not fertilized, it was discovered that the application of fertilizer greatly decreased disease by 27.5% to 54.7%. Pre-sowing applications of calcium ammonium nitrate (26 percent N) or composite NPK (15 percent, 15 percent, 15 percent) on plants resulted in less disease symptoms than pre-sowing applications of NPK (20 percent, 20 percent, 0 percent) .
4.1.9. Soil Test
Farmers conduct soil tests to evaluate the soil's fertility and amount of nutrient availability. They conduct soil tests to check for the presence or absence of one or more nutrients as well as the pH level (Lukin). One of the finest techniques to evaluate the fertility of the land is through soil testing. This evaluation assists in determining the type and quantity of fertilizer and/or limestone that must be used to achieve the highest yield. Soil testing can assist in resolving issues including low yields brought on by a lack of fertility, acidic or basic soils, identification of suitable fertilizer mixtures, and overuse of fertilizer .
4.1.10. Crop Residual Management
Average plant output was found to be higher in the field with crop residue incorporated into the soil compared to crop residue plot treatments than that obtained in residue removal plots. Additionally, soil fertility was consistently higher in crop residue plots than it was in crop residue removal . Crop residues are a rich source of plant nutrients since they include 25% of the nitrogen (N), phosphorous (P), sulfur (S), and potassium (K) that cereal crops absorb. This makes them a significant supply of nutrients (Singh & Singh, 2001). While applying crop residues increases inputs or reduces losses and so helps to maintain or increase soil organic matter content, burning crop leftovers reduces the organic matter inputs to the soil .
4.2. Factors Affecting Adoption of Proper Agriculture Practices
The use of technology and family age has a good relationship with wheat production . Ethiopia's education system has benefited from the introduction of new technologies in the production of wheat, including variety, sowing techniques, mechanization, and row planting . According to Lionberger's research from 1960, adopting suggested habits is favorably correlated with education. The number of years in education, particularly more than eight, was discovered to be virtually universally linked to greater adoption rates. Adoption of improved wheat varieties in Eastern Africa is negatively impacted by the household head's educational level . If farmers had access to the right technology, adoption Training is a crucial component of the extension strategy used in all agricultural development initiatives, and as a result, farmers perform better (Mathur, 1996). The adoption rate of new technology would increase if farmers had access to the proper technologies . The growers' revenue has a favorable impact on the adoption of wheat varieties . If farmers had access to the right technology, adoption of new technologies would grow .
5. Conclusions
The most widely used production techniques among both beneficiaries and non-beneficiaries included seed replacement within three years, improved varietal use, broadcast seed sowing, irrigation at CRI stage, manual weeding, insect pest management using chemical methods, residual management through stubble burning, manual harvesting and use of wheat threshers, use of nitrogen fertilizer in two split doses, and basal application of phosphorus and potassium. Conservation tillage, chemical weeding, biological and cultural insect pest management, residual management through soil integration, and seed treatment and priming were not used by either group. Conversely, both groups had the lowest adoption rates for these activities. Major determining factors for split nitrogen application included age, gender, training received, super-zone beneficiaries, and communication with an extension agent.
Major determining factors for residual management included crop area, ethnicity, and super-zone beneficiaries. Super-zone beneficiaries and interactions with extension personnel were key determinants of sowing date. The largest determinants of enhanced seed variety were crop area and involvement in groups or cooperatives. The two main determinants of seed replacement were ethnicity and training received.
Abbreviations

PMAMP

Prime Minister Agriculture Modernization Project

MoALD

Ministry of Agriculture and Livestock Development

GAP

Good Agricultural Practices

CRI

Crown Root Initiation

Conflicts of Interest
The authors declare no conflicts of interest.
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    Ganesh, M., Anju, K. (2024). Effect of Various Improved Technology in Wheat Production Zone of Nepal. International Journal of Applied Agricultural Sciences, 10(3), 126-137. https://doi.org/10.11648/j.ijaas.20241003.15

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    Ganesh, M.; Anju, K. Effect of Various Improved Technology in Wheat Production Zone of Nepal. Int. J. Appl. Agric. Sci. 2024, 10(3), 126-137. doi: 10.11648/j.ijaas.20241003.15

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    AMA Style

    Ganesh M, Anju K. Effect of Various Improved Technology in Wheat Production Zone of Nepal. Int J Appl Agric Sci. 2024;10(3):126-137. doi: 10.11648/j.ijaas.20241003.15

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  • @article{10.11648/j.ijaas.20241003.15,
      author = {Mahara Ganesh and Karki Anju},
      title = {Effect of Various Improved Technology in Wheat Production Zone of Nepal
    },
      journal = {International Journal of Applied Agricultural Sciences},
      volume = {10},
      number = {3},
      pages = {126-137},
      doi = {10.11648/j.ijaas.20241003.15},
      url = {https://doi.org/10.11648/j.ijaas.20241003.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijaas.20241003.15},
      abstract = {The study was held in 2024 in the Kailari, Gauriganga, Godawari, and Dhangadhi local level of Kailali District of Nepal to evaluate and identify the factors impacting the adoption of a number of sophisticated technologies in the research area. Data were collected from 200 respondents utilizing a semi-structured interview form, using simple random selection. The factors influencing the adoption of better technologies in wheat production were identified using a logistic regression model. Age, gender, ethnicity, and area of cultivation are socioeconomic elements that have been linked to the adoption of appropriate agricultural practices, as well as training, technical advice, and membership. The adoption of seed replacement was positively significant (P<0.1) as a result of the training. The adoption of seed varieties was positively significant (P<0.05) for cultivated area. The farmers who were involved in farmer groups or Cooperative had 2.209 times higher odds for the adoption of improved seed compared to the odds for farmers who were not involved in farmer groups. Advice from the technician had a positively significant (P<0.05) impact on the date of sowing. The use of more frequent irrigation was positively significant (P<0.05) in relation to the age of the household head. The split nitrogen application was positively significantly influenced by super zone membership (P<0.05).
    },
     year = {2024}
    }
    

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  • TY  - JOUR
    T1  - Effect of Various Improved Technology in Wheat Production Zone of Nepal
    
    AU  - Mahara Ganesh
    AU  - Karki Anju
    Y1  - 2024/06/06
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    DO  - 10.11648/j.ijaas.20241003.15
    T2  - International Journal of Applied Agricultural Sciences
    JF  - International Journal of Applied Agricultural Sciences
    JO  - International Journal of Applied Agricultural Sciences
    SP  - 126
    EP  - 137
    PB  - Science Publishing Group
    SN  - 2469-7885
    UR  - https://doi.org/10.11648/j.ijaas.20241003.15
    AB  - The study was held in 2024 in the Kailari, Gauriganga, Godawari, and Dhangadhi local level of Kailali District of Nepal to evaluate and identify the factors impacting the adoption of a number of sophisticated technologies in the research area. Data were collected from 200 respondents utilizing a semi-structured interview form, using simple random selection. The factors influencing the adoption of better technologies in wheat production were identified using a logistic regression model. Age, gender, ethnicity, and area of cultivation are socioeconomic elements that have been linked to the adoption of appropriate agricultural practices, as well as training, technical advice, and membership. The adoption of seed replacement was positively significant (P<0.1) as a result of the training. The adoption of seed varieties was positively significant (P<0.05) for cultivated area. The farmers who were involved in farmer groups or Cooperative had 2.209 times higher odds for the adoption of improved seed compared to the odds for farmers who were not involved in farmer groups. Advice from the technician had a positively significant (P<0.05) impact on the date of sowing. The use of more frequent irrigation was positively significant (P<0.05) in relation to the age of the household head. The split nitrogen application was positively significantly influenced by super zone membership (P<0.05).
    
    VL  - 10
    IS  - 3
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  • Abstract
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    1. 1. Introduction
    2. 2. Methodology
    3. 3. Results
    4. 4. Discussion
    5. 5. Conclusions
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  • References
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