Technical efficiency of wheat growing farmers of Nepal

Adhikari, Surya Prasad; Ghimire, Yuga Nath; Timsina, Krishna Prasad; Subedi, Sanjiv; Kharel, Meena

doi:10.3126/janr.v4i2.33857

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…This applies that technical efficiency of maize farmers in the study area could be increased by 18.53 %. Majority (41%) of farmers are producing technically efficient maize output, however they can still improve their productivity by 18% to realize the full potential of maize production in the study area [11][12][13][14][15].…”

Section: Resultsmentioning

confidence: 99%

Economic and Technical Efficiency of Maize Production in the Eastern District of Bhutan: Stochastic Frontier Approach

Yoezer

2023

SAJSSE

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This research paper seeks to deal with the problem of economic and technical efficiency of maize farmers located in the eastern region of Bhutan. For the objective of this paper, the stochastic frontier parametric approach was employed. The socio-economic and production data were collected from 120 maize growing farmers using simple random sampling technique for the production year 2020-2021. Results show that maize producing farmers are 81.47 % technically efficient and 78.19 % economically efficient in producing maize. Farm size, household size, and training positively influences the maize efficiency. The paper presents supporting evidence for more government policy in terms of provision of subsidies on agrochemicals and providing training for better management skills to the farmers.

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Section: Resultsmentioning

confidence: 99%

Economic and Technical Efficiency of Maize Production in the Eastern District of Bhutan: Stochastic Frontier Approach

Yoezer

2023

SAJSSE

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“…This is in line with the findings by Okello et al (2019) who found out that farmers in Gulu had higher allocative efficiency than their counterparts in Amuru district. Additionally, Adhikari et al (2021) and Tsoho et al (2012) also found out that farm location had a significant association with technical, allocative and economic efficiency among different crops. Accordingly, the results (Table 6) showed that farmers belonging to agricultural groups had 10.16% higher technical efficiency than their fellows who did not belong to the groups.…”

Section: Determinants Of Technical Efficiencymentioning

confidence: 91%

Measuring technical efficiency and its determinants among sweet potatoes farmers in Western Uganda

Midamba¹,

Alela²,

Atukunda³

et al. 2022

Afr. J. Agric. Res.

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Sweet potato is one of the important staple food for majority of Ugandans. As such, it is depended on by many households. Reportedly, sweet potatoes production is low especially in Western Uganda. This may have been attributed to resource misallocation leading to some levels of efficiency or inefficiency. However, the current level of efficiency or inefficiency and its determinants in sweet potatoes production is unknown. As such, this study determined technical efficiency and its determinants among sweet potatoes farmers in western Uganda. Data were collected from 160 households using multistage and random sampling techniques. After data cleaning, 151 questionnaires were considered for the study. Data envelopment analysis was used to determine technical efficiency while Tobit regression model was used to analyse the determinants technical efficiency. The results showed that the mean technical efficiency stood at 55%. Accordingly, household size, farm location, group membership and use of pesticides had a positive and statistically significant effect on technical efficiency while farm size and input prices had an inverse and statistically significant association with technical efficiency. To increase on sweet potatoes yields, farmers should be encouraged to form various groups where they could share production ideas, use pesticides for effective control of weeds and government should subsidise farm inputs to improve on affordability and timely acquisition.

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“…The escalating demand for food at an unprecedented rate emphasizes the importance of timely information on crop growth stages and maturity for effective decision-making, emergency response mechanisms, and efficient monitoring of food production [3]. As a result, various approaches have been employed to model crop yield, including field surveys, expert assessments, farm reports, and forecasting models [4][5][6]. In recent years, integrating remote sensing and machine learning tools has sparked a fundamental shift in sustainable agriculture.…”

Section: Introductionmentioning

confidence: 99%

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

Sahbeni,

Székely,

Musyimi

et al. 2023

AgriEngineering

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Effective crop monitoring and accurate yield estimation are fundamental for informed decision-making in agricultural management. In this context, the present research focuses on estimating wheat yield in Nepal at the district level by combining Sentinel-3 SLSTR imagery with soil data and topographic features. Due to Nepal’s high-relief terrain, its districts exhibit diverse geographic and soil properties, leading to a wide range of yields, which poses challenges for modeling efforts. In light of this, we evaluated the performance of two machine learning algorithms, namely, the gradient boosting machine (GBM) and the extreme gradient boosting (XGBoost). The results demonstrated the superiority of the XGBoost-based model, achieving a determination coefficient (R2) of 0.89 and an RMSE of 0.3 t/ha for training, with an R2 of 0.61 and an RMSE of 0.42 t/ha for testing. The calibrated model improved the overall accuracy of yield estimates by up to 10% compared to GBM. Notably, total nitrogen content, slope, total column water vapor (TCWV), organic matter, and fractional vegetation cover (FVC) significantly influenced the predicted values. This study highlights the effectiveness of combining multi-source data and Sentinel-3 SLSTR, particularly proposing XGBoost as an alternative tool for accurately estimating yield at lower costs. Consequently, the findings suggest comprehensive and robust estimation models for spatially explicit yield forecasting and near-future yield projection using satellite data acquired two months before harvest. Future work can focus on assessing the suitability of agronomic practices in the region, thereby contributing to the early detection of yield anomalies and ensuring food security at the national level.

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Technical efficiency of wheat growing farmers of Nepal

Cited by 3 publications

References 13 publications

Economic and Technical Efficiency of Maize Production in the Eastern District of Bhutan: Stochastic Frontier Approach

Economic and Technical Efficiency of Maize Production in the Eastern District of Bhutan: Stochastic Frontier Approach

Measuring technical efficiency and its determinants among sweet potatoes farmers in Western Uganda

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

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