Agricultural Productivity and Poverty Reduction in <scp>N</scp>epal

Devkota, Satis; Upadhyay, Mukti P.

doi:10.1111/rode.12062

Cited by 25 publications

(30 citation statements)

References 17 publications

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“…However, previous papers based on the efficiency wage hypothesis have largely not Table 9 (2) 10th 0.0705*** 0.4086 Table 9 (3) 25th 0.0571*** 0.3308 (2) and (3) come from Table 7. considered factors explaining productivity, except for nutrient intake. Devkota and Upadhyay (2013) measured the effect of agricultural labor productivity on poverty alleviation in Nepal. However, their measurement used cross-sectional data, in which it is difficult to control for heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

“…Such analysis would reveal the factors underlying different agricultural productivity at the regional level. Both the World Bank (2006) and Devkota and Upadhyay (2013) report that land and labor productivity in the agricultural sector have slumped, although neither study measures or estimates specific changes in agricultural productivity. We expect that our ongoing research, using panel data sets to obtain an objective picture of the present condition, will reveal the factors underlying this developmental trough.…”

Section: Discussionmentioning

confidence: 99%

“…Agricultural productivity affects the level of poverty in Nepal (Devkota & Upadhyay, 2013). In reaching this conclusion, labor productivity was assumed as agricultural productivity.…”

mentioning

confidence: 99%

“…However, we could subtract a household's particular pattern of not only food consumption but also heterogeneity caused by a household's unique characteristics or agricultural land condition. Devkota and Upadhyay (2013) estimated agricultural productivity using the Cobb-Douglas and translog production functions to quantify productivity's effect on poverty reduction. They then identified the determinants of agricultural productivity that affect poverty reduction.…”

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confidence: 99%

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Agricultural Productivity Growth and Household Food Security Improvement in Nepal

Morioka

Kondo

2017

Review Development Economics

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Nepal is one of the poorest countries in the world. Using the panel data of the Nepal Living Standard Measurement Survey (NLSS), the poverty rate in 1995-96 was estimated to be about 40%, while in 2003-04 it was estimated at roughly 30%. Political instability has prevented the development of industry and threatened the food security of poor people, but scholarship on the factors contributing to food

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Agricultural productivity affects the level of poverty in Nepal (Devkota & Upadhyay, 2013). In reaching this conclusion, labor productivity was assumed as agricultural productivity.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Agricultural Productivity Growth and Household Food Security Improvement in Nepal

Morioka

Kondo

2017

Review Development Economics

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“…This is done with inadequate data to substantiate this fact. Satis Devkota, Mukti Upadhyay [10] specifies the agriculture productivity as well as how reduction was reduced in Nepal. Methodology used is cobs-Douglas and trans-log production function Also paper identifies the determinant of productivity which helps in the reduction of poverty using Sen's property index.…”

Section: Literature Surveymentioning

confidence: 99%

Determining the Yield of the Crop using Artificial Neural Network Method

Rajan¹,

Nandi²,

Chauhan³

et al. 2019

IJEAT

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The agricultural system is complex and comprehend since it deals with large data that comes from several factors. Lot of techniques and have been used to identify any interactions between factors affecting yields with crop performance. The major objective of this paper is to help us predict the yield of a particular crop before even cultivating it for its production. We are using artificial neural networks for forwarding and implementing a system that will help the farmers in finding their crop yields according to their given data as input in the system and the system will give output based on previous data. The method used in this crop yield system is an artificial neural network and the algorithm used is feed forward and back propagation. Provide the input of data sets and the desired outcome of the system. Compute the error between the actual and desired outcome of the system. Amendment of the weight associated with different inputs and different functions. Compare the errors and the tolerance ratio of the output. Various machine learning techniques have been used in the past for calculating the crop yield using remote data. However, these methods are less useful and effective for predicting the yield of maize and for some other crops, which is cultivated at different times in various fields.The major application of this crop yield system is that it will help us to predict the yield before even cultivating it by studying the previous data collected such as soil fertility, pH level.

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The Relation Between Property Rights, Farm Size and Technical Efficiency for the Developing Countries' Agricultural Sector

Dourandish

Saghaian

Forushani

et al. 2020

J of Intl Development

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Previous studies have shown that the quality of property rights, farm size and market imperfections significantly affect agricultural efficiency. This article investigates the interaction between the quality of property rights and size when the countries are heterogeneous in degree of market imperfection, using a multilevel statistical approach for a sample of 84 developing countries. The results show a positive relation between technical efficiency and quality of property rights and a negative relation between farm size and quality of property rights. Furthermore, the results reject the regularly stated inverse relation between farm size and technical efficiency. In addition, the results show that market imperfections significantly affect the relations between technical efficiency and the explanatory variables. According to the results, increasing the farm size in countries with well‐defined property rights reduces agricultural efficiency. On the other hand, in the majority of developing countries with low quality of property rights, the relation between farm size and technical efficiency is statistically significant and positive. © 2020 John Wiley & Sons, Ltd.

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Agricultural Productivity and Poverty Reduction in Nepal

Cited by 25 publications

References 17 publications

Agricultural Productivity Growth and Household Food Security Improvement in Nepal

Agricultural Productivity Growth and Household Food Security Improvement in Nepal

Determining the Yield of the Crop using Artificial Neural Network Method

The Relation Between Property Rights, Farm Size and Technical Efficiency for the Developing Countries' Agricultural Sector

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