Advances in agricultural machinery management: A review

Bochtis, Dionysis; Sørensen, Claus Aage Grøn; Busato, Patrizia

doi:10.1016/j.biosystemseng.2014.07.012

Cited by 210 publications

(81 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In agriculture they can be employed to analyse: performance of agricultural machines (Ampatzidis et al, 2014;Coen et al, 2008 andEbrahimi et al, 2013), agronomic operations ( Van't Ooster et al, 2014 andBochtis et al, 2014), production management (Tedeschi et al, 2011 andReynoso-Campos et al, 2004), production economy (Valdivia et al, 2012 andLi, 2011) possibility of greenhouse emission reduction (Crosson et al, 2011) and other areas of agricultural production and systems (Wallach et al, 2014;Rosenzweig et al, 2013;Rossing et al, 2007). One of the tools used in numerical modelling and analysis of various systems is the Matlab environment, widely used in the area of agriculture (Giusti and Marsili-Libelli, 2015;Menesatti et al, 2014;Zhang et al, 2011;Papadopoulos et al, 2011;Cool et al, 2014 andOdegard andvan der Voet, 2014), including modelling of the renewable energy systems (Kıyan et al, 2013;Woinaroschy, 2014;Da Silva and Fernandes, 2010;Nakoul et al, 2014;Taghavifar andMardani, 2014 andSefeedpari et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Carbon dioxide emission reduction by heating poultry houses from renewable energy sources in Central Europe

Kapica

Pawlak

Ścibisz

2015

Agricultural Systems

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Carbon dioxide emission reduction by heating poultry houses from renewable energy sources in Central Europe

Kapica

Pawlak

Ścibisz

2015

Agricultural Systems

View full text Add to dashboard Cite

“…Likewise, alleviating the agricultural labor demand due to the increasingly lucrative alternate employment forms of the country (M. W. . Suitable agricultural mechanization has been identified as a crucial development plan in Pakistan (Bochtis, Sørensen, & Busato, 2014). Despite the impressive growth of mechanical technology, the users of tube well, tractor and thresher technologies are only about 34 percent, 38 percent and 17 percent, respectively, of the total farms.…”

Section: Introductionmentioning

confidence: 99%

Study on the Socioeconomic Factors Affecting Adoption of Agricultural Machinery

2020

JESD

View full text Add to dashboard Cite

In the context of south Asia, There is enough support for suitable agricultural machinery for small farmers. These kind of agricultural machineries can improve the return of investment in land and labor, although large capital investments are still required that can impede ownership. The growing demand for machinery has resulted in comparatively more developed rental marketplaces for irrigation, tillage and other post-harvest procedures. Numerous small businesses are therefore accepting access to farm machinery that would otherwise be incredibly expensive to buy through service fee arrangements, although there is still room for expansion. In order to facilitate the advancement and investment of such machinery more effectively, it is necessary to better understand the associated factors with the purchase of agricultural machinery and the provision of services. Firstly, current paper reviews country's policy structure which enabled the existence of such machinery markets. It then uses stratified random sample of 305 wheat producing households for the survey from six districts, identifying variables associated with the adoption of the most common smallholder agricultural machinery e irrigation pumps, threshers, and power tillers. Results of multinomial probit model show that education of farmer, member of farmer' organization, livestock ownership, farm size and being part of non-farm work activities all were significantly positive in the adoption of farm machinery. Findings also suggest that institutions and policy making authorities not only need to focus on short projects to encourage adoption of machinery, also there must be a continuous attention to improve physical and civilian infrastructure & services, and ensuring the availability of credit to create an favorable conditions where agricultural machinery is most likely to be used.

show abstract

“…In order to improve fuel consumption, machinery life, and work efficiency, it is necessary to strengthen the fleet management during agricultural machinery operations. Currently, GNSS [4][5][6] and ISOBUS [7,8] based fleet management are widely used and can realize real-time visibility of vehicle location, status, and diagnostics. However, it cannot record subtle but important operating behaviors [9] , such as longtime stalling or idling, sudden acceleration, sharp turning, and etc.…”

Section: Introduction mentioning

confidence: 99%

Behavior modelling and sensing for machinery operations using smartphone’s sensor data: A case study of forage maize sowing

Wu¹,

Chen²,

Wang³

et al. 2019

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

Large-scale agricultural machinery cooperatives require technical statistic report of agricultural machinery operations to improve the efficiency of fleet management. This research proposed a smartphone-based solution to build the behavior model for agricultural machinery operations by using the embedded sensors including the GNSS, the accelerometer, and the microphone. The whole working process of agricultural machinery operation was divided into four stages: preparation, operation, U-turn, and transfer, each of which may contain the behaviors of stalling and idling. Field experiments were carried out by skilled operators, whose operations were typical agricultural machinery operations that could be used to extract behavior features. Butterworth low-pass filter was used to smooth the output from the accelerometer. Then, the operating data were collected through an APP when sowing the forage maize as a case study. Four stages of machinery operation can be preliminarily classified by using GNSS speed, while the identification of behaviors such as sudden acceleration and longtime idling that may increase fuel consumption, reduce machinery life, or decrease the working efficiency, requires extra information such as acceleration and sound intensity. The results showed that the jerk of accelerating can describe the severity of the sudden acceleration, the standard deviation of forward acceleration can reflect the smoothness of operation, the upward acceleration can be used to identify behaviors of stalling and idling, and the sound intensity during idling can capture the behavior of goosing the throttle. Further, the operating behavior figure can be drawn based on the above parameters. In conclusion, this research constructed several behavior models of agricultural machinery and operators by using smartphone's sensor data and established the base of the online assessing and scoring system for agricultural machinery operations. modelling and sensing for machinery operations using smartphone's sensor data: a case study of forage maize sowing. Int J Agric & Biol Eng, 2019; 12(6): 66-74. Biographies: Caicong Wu, PhD, Associate Professor, research interest: location-based service of farm machinery, Email: wucc@cau.edu.cn; Zhibo Chen, PhD candidate, research interest: location-based service of farm machinery, Email: bobodi@qq.com; Dongxu Wang, Master candidate, research interest: location-based service of farm machinery, Email: dongxu_wang@ cau.edu.cn; Zhihong Kou, Master candidate, research interest: location-based service of farm machinery, Email: 603040848@qq.com; Yaping Cai, PhD candidate, research interest: mobile information collection, Email: caicai_pku@ 163.com.

show abstract

Advances in agricultural machinery management: A review

Cited by 210 publications

References 51 publications

Carbon dioxide emission reduction by heating poultry houses from renewable energy sources in Central Europe

Carbon dioxide emission reduction by heating poultry houses from renewable energy sources in Central Europe

Study on the Socioeconomic Factors Affecting Adoption of Agricultural Machinery

Behavior modelling and sensing for machinery operations using smartphone’s sensor data: A case study of forage maize sowing

Contact Info

Product

Resources

About