Farm-Level Technical Efficiency and Its Determinants of Rice Production in Indo-Gangetic Plains: A Stochastic Frontier Model Approach

Abstract

This research was conducted to explore the factors affecting the technical efficiency (TE) of rice producers and its determinants at the farm level. We used a multi-stage sampling procedure to collect cross-sectional data from 800 rice growers in the Uttar Pradesh state of India, and a stochastic frontier model (SFA) was applied. The results showed that the mean technical efficiency was 72%, suggesting scope for a substantial increment in rice productivity exists while using the current level of inputs and technologies. Furthermore, the MLE results revealed that labor, irrigation, and hybrid seeds had a constructive impact on technical efficiency, while experience and tenure status showed a negative impact on technical efficiency. As unraveled by the results of the study, it can be concluded that the technical efficiency of rice farmers can be improved through timely access to credit and agricultural information delivered to them via extension services. The study, therefore, recommends that the government provide subsidized agrochemicals and focus on developing a robust network of extension services throughout the local districts for proper dissemination of inputs. About 12% of India’s rice is produced in the Uttar Pradesh state. So, this study could be an essential tool for the agriculture sector, which could help to solve rice productivity problems for future generations.

Assessment of climate variability and the determinants of rice productivity in Southeastern Nigeria

“…For the probability effects of the independent variables on rice productivity; sex, category of the farmer, age of the farmer, educational qualification, membership of the cooperative society, and extension visits were the significant determinants of rice productivity in the study area (Table 9). This corroborates the findings of earlier studies (Ita et al, 2013;Osanyinlusi and Adenegan, 2016;Niang et al, 2017;Balogun et al, 2021;Chandel et al, 2022).…”

“…While water availability/irrigation, labour, capital, rice prices, fertilizer, and acreage are positively correlated with rice yield (Shaikh et al, 2016;Kamal Jan and Khan, 2019), the temperature was reported to have a negative correlation (Mech, 2017). Labour, irrigation, and hybrid seeds/quantity of seeds have a constructive impact on the technical efficiency of rice farmers (Balogun et al 2021) but experience and tenure status have a negative impact on technical efficiency in a study carried out in the Indo-Gangetic plains (Chandel et al, 2022). Interannual variations in rainfall and spatial variations in soil moisture content affect rice yield (Niang et al, 2018).…”

Technical Efficiency and Economic Analysis of Rice Crop in Khyber Pakhtunkhwa: A Stochastic Frontier Approach

“…The coefficient of age was positive and significant, which indicates that with an increase in rice grower's age, technical efficiency decreases. The results of the study for age are supported by [12,61]. They reported that older farmers are less willing to adopt new technology and inputs.…”

“…A study was conducted by [61] on the technical efficiency of rice crop in Uttar Pradesh State of India. Cross sectional data from 800 rice growers was analyzed.…”

Energy consumption and pollution control from the perspective of industrial economic activity: An empirical study of China’s coastal provinces

“…As for the estimation of industrial resource and environmental efficiency, many scholars currently use ecological footprint method, Stochastic Frontier Analysis (SFA), life cycle method and decoupling model. For example, Cao et al (2022), Bibi et al (2022), Chishti et al (2021), Dagar et al (2022), Chandel et al (2022) used the SFA model to explore the energy efficiency of industrial sectors in India north. However, the method of Data Envelopment Analysis (DEA) has increasingly been adopted to measure the efficiency of industrial resources and environment.…”