Energy efficiency of rice production in farmers’ fields and intensively cropped research fields in the Philippines

Quilty, James R.; McKinley, Justin D.; Pede, Valerien O.; Buresh, R. J.; Correa, Teodoro Q.; Sandro, Joseph M.

doi:10.1016/j.fcr.2014.08.001

Cited by 60 publications

(49 citation statements)

References 36 publications

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“…Until now, several studies have explored the use of energy and its efficiency for production of crops at the domestic as well as international levels [13,14], e.g. Indian cotton, potatoes, beans, and wheat [15]; Italian maize, beans, and wheat [16]; Turkish sugar beets, apricots, and cotton [17][18][19]; Philippines rice [20] and Chinese rice [21]. Among these, only a few have discussed sugar beet and wheat GHG emissions [22,23].…”

Section: Introductionmentioning

confidence: 99%

Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis

Ilahi

Raza

et al. 2019

Sustainability

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Energy is a major component in enhancing agricultural productivity for the rapidly growing world population. From that fact, a comprehensive analysis of energy inputs and outputs is required to conserve energy for future generations without threatening the food supply. Therefore, this study was performed in wheat production across important cropping zones of Punjab, Pakistan. In this study, the energy use pattern of wheat production was analyzed, and the degrees of technical efficiency of Decision Making Units (DMUs) were examined using Data Envelopment Analysis (DEA). Based on the results of the DEA analysis, the inefficient energy inputs were identified and further explored with the core objective of a significant reduction of excess valuable resources. Data were collected from conducting a face-to-face questionnaire of 200 farmers. The farms for sample were chosen randomly by a stratified normal approach. The results disclosed that the input energy of 34,430.98 MJ ha−1 was used up for wheat production with an output energy of 48,267.05 MJ ha−1. Energy use efficiency, specific energy, energy productivity, and net energy gain in wheat production were calculated as 1.4 MJ kg−1, 9.27 MJ kg−1, 0.10 MJ kg−1 and 13,836.07 MJ kg−1, respectively. The average technical, pure technical, and scale efficiency of DMUs were 0.668, 0.776, and 0.828, respectively, and 0.74% of consulted DMUs were functioning at decreasing returns to scale. Additionally, the significant energy consumption belongs to fertilizer, and diesel fuel, which contribute 65% of the total energy input. If these inputs are applied and managed in line with ours optimize value (29,388.5 MJ ha−1) could save 14.65% resources, which will eventually add the equal quantity in wheat-yield. The total Greenhouse Gas (GHG) emissions were calculated to be 866.43 kg CO2-eq ha−1. In conclusion, the results of the present study suggest that there is sensible capacity for enhancing the energy efficiency of wheat production in Pakistan by accompanying the recommendations for economical energy management, sustainable and efficient use of energy is extremely encouraged.

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Section: Introductionmentioning

confidence: 99%

Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis

Ilahi

Raza

et al. 2019

Sustainability

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“…Numerous attempts have been made to measure technical efficiency (TE) and other efficiency estimates in farming (Alvarez, ; Balde et al., ; Coelli and Battese, ; Hossain and Rahman, ; Idiong, ; Karagiannis and Tzouvelekas, ; Michler and Shively, ; Quilty et al., ); to this end, one of the common econometric approaches is the stochastic frontier analysis (Aigner et al., ; Meeusen and van den Broeck, ). Previous studies have contributed to the understanding of how large TE is; how different TE levels are among individual farmers; and what are the factors that underlie the differences.…”

Section: Introductionmentioning

confidence: 99%

Spatial dependency and technical efficiency: an application of a Bayesian stochastic frontier model to irrigated and rainfed rice farmers in Bohol, Philippines

Pede

Areal

Singbo

et al. 2018

Agricultural Economics

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We investigated the role of spatial dependency in the technical efficiency estimates of rice farmers using panel data from the Central Visayan island of Bohol in the Philippines. Household-level data were collected from irrigated and rainfed agro-ecosystems. In each ecosystem, the geographical information on residential and farm-plot neighborhood structures was recorded to compare household-level spatial dependency among four types of neighborhoods. A Bayesian stochastic frontier approach that integrates spatial dependency was used to address the effects of neighborhood structures on farmers' performance. Incorporating the spatial dimension into the neighborhood structures allowed for identification of the relationships between spatial dependency and technical efficiency through comparison with nonspatial models. The neighborhood structure at the residence and plot levels were defined with a spatial weight matrix where cut-off distances ranged from 100 to 1,000 m. We found that spatial dependency exists at the residential and plot levels and is stronger for irrigated farms than rainfed farms. We also found that technical inefficiency levels decrease as spatial effects are more taken into account. Because the spatial effects increase with a shorter network distance, the decreasing technical inefficiency implies that the unobserved inefficiencies can be explained better by considering small networks of relatively close farmers over large networks of distant farmers. JEL classifications: C01, C11, C23, C51, D24

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“…Nawn et al (2016) pointed out, that in the literature on sustainability of agriculture, both laborers and workers are conspicuously absent and found negative surplus to be near-universal in developing country. In study of Quilty et al (2014) manual labour accounted for less than 3 % of the total input and is argued that future production systems will require more mechanisation. Deike et al (2008) found the largest shares of energy input in integrated farming treatments were diesel fuel and mineral fertilizers.…”

Section: Discussionmentioning

confidence: 99%

Energy Use of Different Farming Systems in Long-Term Trial

L¹,

Míša²

2016

Acta Univ. Agric. Silvic. Mendelianae Brun.

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This study evaluate the effects of different types of agricultural systems and their managements on energy balance over 45-year period (1970–2015). Agricultural systems were (i) spring barley and (ii) winter wheat monocultures with different types of organic supply (straw incorporation, straw incorporation + green manuring, green manuring and control without organic supply) and (iii) Norfolk four-course system. Averaged across each half of study period, total energy inputs ranged between 10.2 GJ ha-1 year-1 for barley in Norfolk to 22.9 GJ ha-1 year-1 for wheat in monoculture with green manuring. The results indicate increase of indirect inputs in case of wheat and barley in Norfolk. The only system where the indirect input energy became lower was barley monoculture. Total energy outputs ranged between 198 GJ ha-1 year-1 for wheat in Norfolk to 88 GJ ha-1 year-1 for barley in monoculture. Norfolk system had higher output/input ratio than monocultures during whole study period. In almost all systems the effectiveness of energy use decreased during study period when comparing two halves. Yields of cereals throughout all systems were negatively dependent on energy input.

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Energy efficiency of rice production in farmers’ fields and intensively cropped research fields in the Philippines

Cited by 60 publications

References 36 publications

Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis

Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis

Spatial dependency and technical efficiency: an application of a Bayesian stochastic frontier model to irrigated and rainfed rice farmers in Bohol, Philippines

Energy Use of Different Farming Systems in Long-Term Trial

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