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Journal articles on the topic 'Farm machinery'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 May 2022

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1

Paman, Ujang, Khairizal Khairizal, and Hajry Arief Wahyudy. "KEBUTUHAN MESIN PERTANIAN DAN TENAGA UNTUK MEKANISASI USAHATANI PADI SKALA KECIL DI KABUPATEN KAMPAR, PROVINSI RIAU." DINAMIKA PERTANIAN 33, no. 2 (September 24, 2019): 129–36. http://dx.doi.org/10.25299/dp.2017.vol33(2).3825.

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Farm machinery is one of the important power sources to carry out farm operations today. The availability of farm power in adequate number is prerequisites for providing enough power in perform rice farming operation. This paper attempts to evaluate the farm machinery and power requirement for performing operations of small-scale rice farming in Kampar Regency, Riau Province. Data were obtained from field survey in 15 districts from 21 districts of Kampar Regency. Village surveys were chose for each selected district which are rice production center and farm machines have been used in working rice farm operations. Samples were purposively selected and consisted of 30 farmers, 5 extension staffs and 20 machine operators. They were interviewed to collect field data on September 2015 by using questionnaires. The results show that the number of farm machinery was not sufficient to fulfill requirement and provide enough power for farm operations. From farm machinery available, it provided power about 0.61 hp.ha-1, while minimum power required for efficient rice farming is about 0.8 hp.ha-1. The results suggest that the number and type of farm machinery must be increased with financial support and involve private sectors.

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Ismail, Ujang Paman, Khairizal Khairizal, and Hajry Arief Wahyudy. "KEBUTUHAN MESIN PERTANIAN DAN TENAGA UNTUK MEKANISASI USAHATANI PADI SKALA KECIL DI KABUPATEN KAMPAR, PROVINSI RIAU." DINAMIKA PERTANIAN 33, no. 2 (November 1, 2017): 11. http://dx.doi.org/10.25299/dp.2017.vol33(2).813.

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Farm machinery is one of the important power sources to carry out farm operations today. The availability of farm power in adequate number is prerequisites for providing enough power in perform rice farming operation. This paper attempts to evaluate the farm machinery and power requirement for performing operations of small-scale rice farming in Kampar Regency, Riau Province. Data were obtained from field survey in 15 districts from 21 districts of Kampar Regency. Village surveys were chose for each selected district which are rice production center and farm machines have been used in working rice farm operations. Samples were purposively selected and consisted of 30 farmers, 5 extension staffs and 20 machine operators. They were interviewed to collect field data on September 2015 by using questionnaires. The results show that the number of farm machinery was not sufficient to fulfill requirement and provide enough power for farm operations. From farm machinery available, it provided power about 0.61 hp.ha-1, while minimum power required for efficient rice farming is about 0.8 hp.ha-1. The results suggest that the number and type of farm machinery must be increased with financial support and involve private sectors.

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3

Cox, S. W. R. "Farm machinery (12th edition)." Computers and Electronics in Agriculture 9, no. 2 (September 1993): 189. http://dx.doi.org/10.1016/0168-1699(93)90007-n.

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4

Ma, Wanglin, Alan Renwick, and Quentin Grafton. "Farm machinery use, off-farm employment and farm performance in China." Australian Journal of Agricultural and Resource Economics 62, no. 2 (March 22, 2018): 279–98. http://dx.doi.org/10.1111/1467-8489.12249.

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5

Fountas, S., C. G. Sorensen, Z. Tsiropoulos, C. Cavalaris, V. Liakos, and T. Gemtos. "Farm machinery management information system." Computers and Electronics in Agriculture 110 (January 2015): 131–38. http://dx.doi.org/10.1016/j.compag.2014.11.011.

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6

Waleed, Muhammad, Tai-Won Um, Tariq Kamal, and Syed Muhammad Usman. "Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things." Symmetry 13, no. 3 (March 1, 2021): 403. http://dx.doi.org/10.3390/sym13030403.

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In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.

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7

Zhang, Fan, Gui Fa Teng, Jian Bin Ma, and Jie Yao. "Research on Multitask Collaborative Scheduling Problem with Heuristic Strategies." Applied Mechanics and Materials 66-68 (July 2011): 758–63. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.758.

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According to problems existed in the current farm machinery scheduling process, a new farm machinery scheduling scheme is adopted in this dissertation. The collaborative scheduling model of farm machinery is established and multitask collaborative scheduling algorithm is designed through analyzing the differences between Vehicle Scheduling Problem and agricultural machinery scheduling in the dissertation. Earliest Start Time First and minimal resource allocated capacity first strategies are used in the farm machinery scheduling. The algorithm is useful for the case of machinery owner with sufficient farm machinery. The experiment proves that the collaborative scheduling algorithm is more effective than the serial scheduling algorithm.

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8

Ma, Wan Min, and Wan Zheng Ma. "Research on Supervision Current Situation and Countermeasures of Agricultural Modernization in China." Advanced Materials Research 860-863 (December 2013): 3104–7. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.3104.

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Agricultural machinery production safety work is the work of national security, an important part of the production. Based on summarizing the research status of farm machinery safety supervision, this paper analysis the main problems of farm machinery safety supervision ,combining with the actual farm machinery safety supervision we propose improvement measures ,we focus on technology innovation to improve service farm machinery safety supervision level, to promote the cause of farm machinery safety supervision development, to improve agricultural production safety, We should be better for farmers to get rich, agricultural development and social harmony and stability in rural areas to provide security services.

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9

Kinga, N., and W. Chetem. "An Economic Analysis Of Government Custom Hiring Services For Different Farm Machineries In Bhutan." SAARC Journal of Agriculture 17, no. 2 (February 3, 2020): 93–101. http://dx.doi.org/10.3329/sja.v17i2.45297.

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Farmers in Bhutan trend to own agricultural machineries though the individual area under farm mechanization is not enough. Hiring out or in of farm machineries as a business enterprise need to be advocated. Therefore, an effort was made in this study to generate some parameters regarding farm machinery business at farm level based on practical experience. Bhutanese Government has endorsed some users’ parameters including cost and return which are used to guide the custom rates for different machineries for farmers and service providers. The study’s objective was to generate different empirical formula for different farm machineries which shall help to propose varying custom hiring rates based on the projected operation days based on the government endorsed parameters. It is also to ascertain whether the present rates charged by Farm Machinery Corporation Limited bring a positive return to its custom hiring business for different machines. The study revealed that the operation cost of farm machinery includes both fixed cost and variable cost. Cost, return and break-even data for different farm machineries give a confident opinion for the owners and Farm Machinery Corporation Limited (FMCL) to take up the hiring scheme as a profitable business. The custom hiring rate also gives a good guide on the custom hiring rates to be charged based on the operating days in the country. On endorsed hiring rates and parameters by government, FMCL shall be able to make a huge profit annually especially on tractors and mini combine harvesters as compared to other machineries by Nu 0.51M and 0.39 M respectively. All other machines are also making a profit annually under the government endorsed parameters. However, there is also an opportunity to reduce the custom rates to bring down the overall cost of cultivation of respective crops using the machines. SAARC J. Agri., 17(2): 93-101 (2019)

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10

ALSUHAIBANI, SALEH A. "Farm Machinery Breakdown Classification – Seed drills." JOURNAL OF ADVANCES IN AGRICULTURE 6, no. 1 (April 30, 2016): 813–21. http://dx.doi.org/10.24297/jaa.v6i1.5389.

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Work job orders for 196 seed drills collected by HADCO (Hail Agricultural Development Company), Saudi Arabia, were used in this study. The work job orders were classified in terms of the parts used and the cost of each item. Distribution of repair and maintenance as listed in the work-job orders was classified as repairs by 55% while 45% was found to be maintenance. Moreover, Classification of repair and maintenance for parts of seed drills was 66% for rapair jobs and 34% for manitinance jobs. Cost ratio term was developed, by dividing the repair or maintenance cost by the seed drill purchase price. The results indicate that the average cost ratio was affected by annual working area. Also repair and maintenance cost decreased as increasing the number of seed drills every year, and due to the increasing number of working area, the costs increased after about 430 ha/year.

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11

Shujun, LI. "Development of Farm machinery in China." IFAC Proceedings Volumes 46, no. 4 (2013): 9–10. http://dx.doi.org/10.3182/20130327-3-jp-3017.00005.

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12

Manor, G., and D. Nir. "Accuracy of performance of farm machinery." Journal of Terramechanics 24, no. 1 (January 1987): 87–94. http://dx.doi.org/10.1016/0022-4898(87)90061-9.

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13

GC, Arun, Jun-Ho Yeo, and Kiran Ghimire. "Determinants of Farm Mechanization in Nepal." Turkish Journal of Agriculture - Food Science and Technology 7, no. 1 (January 15, 2019): 87. http://dx.doi.org/10.24925/turjaf.v7i1.87-91.2131.

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The paper attempts to explore the factors affecting the farm mechanization in Nepal, where agriculture is the primary livelihood of most people and the mechanization is crucial for efficient production and productivity. The government has also introduced a separate policy for agriculture mechanization in 2014. A primary data was collected from 300 households and analysed to assess and quantify the determinants of the farm mechanization. A descriptive analysis was carried out for understanding the data and the results were interpreted. Similarly, the multiple regression was executed to assess the factors affecting total investment in the farm machinery. The five different models were specified and compared for the better results. Moreover, to have deeper insight, the farm machinery was categorized into light machinery, heavy machinery and animal power. The results showed that light machinery is an essential part of Nepali farming system. Likewise, the presence of animal power, income per capita, per capita farm area, adaptation due to change in temperature, Household size, Farm area and income are significant determinants for total investment in farm mechanization.

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14

Akram, Muhammad Waqar, Nida Akram, Hongshu Wang, Shahla Andleeb, Khalil Ur Rehman, Umair Kashif, and Syed Farhaan Hassan. "Socioeconomics Determinants to Adopt Agricultural Machinery for Sustainable Organic Farming in Pakistan: A Multinomial Probit Model." Sustainability 12, no. 23 (November 24, 2020): 9806. http://dx.doi.org/10.3390/su12239806.

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Urbanization and low productivity are real threats to the sustainability of organic farming. The adoption of farm machinery plays a vital role in overcoming these threats to ensure a sustainable and more profitable organic farming model. Farm machinery can also increase farmland yield and reduce the need for labor, although the requirement of significant capital investment often prevents small farmers from buying machinery. There is an increased need to comprehend all relevant elements associated with farming machinery procurement and service delivery. In this article, we provide insight into the impact of different variables of farmers on the adoption of agricultural equipment. A total of 301 organic farmers were surveyed in three districts of Punjab, Pakistan. It was found that the most common machinery concerned herein are tube-well/pumps, tractors, tillage machinery, and thrashers/harvesters. Results from a multinomial probit estimation showed that farm machinery ownership is positively correlated with capital assets, civil infrastructure, alternative sources of power, and credit facility. The findings indicated that policymakers and stakeholders should not concentrate merely on short term planning, such as improving agricultural machinery’s adoption rate. Still, they should also strive to upgrade physical infrastructure and facilities and provide credit services to create an enabling environment that can empower the citizen in adopting large scale use of agricultural machinery for long term sustainability of organic farming.

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15

A.Yousif, Lotfie. "Crop-machinery management system for field operations and farm machinery selection." Journal of Agricultural Biotechnology and Sustainable Development 5, no. 5 (September 30, 2013): 84–90. http://dx.doi.org/10.5897/jabsd2013.0205.

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16

Řezník, Tomáš, Lukáš Herman, Martina Klocová, Filip Leitner, Tomáš Pavelka, Šimon Leitgeb, Kateřina Trojanová, et al. "Towards the Development and Verification of a 3D-Based Advanced Optimized Farm Machinery Trajectory Algorithm." Sensors 21, no. 9 (April 23, 2021): 2980. http://dx.doi.org/10.3390/s21092980.

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Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostěnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.

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17

Siemens, John, Keith Hamburg, and Trevor Tyrrell. "A Farm Machinery Selection and Management Program." Journal of Production Agriculture 3, no. 2 (April 1990): 212–19. http://dx.doi.org/10.2134/jpa1990.0212.

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18

Ismail, Z. E., M. M. Ibrahim, and S. A. Embaby. "ECONOMIC EVALUATION AND SELECTION OF FARM MACHINERY." Misr Journal of Agricultural Engineering 26, no. 4 (October 1, 2009): 1667–81. http://dx.doi.org/10.21608/mjae.2009.107198.

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19

D. A. Bender, D. E. Kline, and B. A. McCarl. "POSTOPTIMAL LINEAR PROGRAMMING ANALYSIS OF FARM MACHINERY." Transactions of the ASAE 33, no. 1 (1990): 0015–20. http://dx.doi.org/10.13031/2013.31286.

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20

Kadhim, Z. R. "MARGIN OF SAFETY OF HIRING DECISION OF AGRICULTURAL MACHINERY SERVICES BY RICE FARMERS IN ALNAJAF AL-ASHRAF PROVINCE." IRAQI JOURNAL OF AGRICULTURAL SCIENCES 52, no. 3 (June 22, 2021): 756–62. http://dx.doi.org/10.36103/ijas.v52i3.1367.

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The main objective of this study is to compute margin of safety of hiring decision of agricultural machinery services in Iraq. A cost-volume-profit analysis by using breakeven point technique has been followed to find out the margin of safety by using cross- section data in rice production farms in Alnajaf province. Results showed that the break-even point for different types of original agricultural field machinery were 427, 6.5, and 221 hours for tractor and machinery of soil preparation, farm sprayer, and combine harvester, respectively, while the values of break-even point for different types of used agricultural field machinery were 309, 10, and 319 hours for tractor, farm sprayer, and combine harvester, in that order. Results also pointed out that the negative sign of safety margin of areas of all machineries indicated that there are losses faced by small scale farmers in case of ownership these machineries, and the minimum lose amounted about $ 316116 for original tractor, $ 52611 for used tractor, $ 220.5 for original farm sprayer, $ 88 for used farm sprayer, $ 664664 for original combine harvester and $ 584650 for used combine harvester. The study recommended that it is useful for small scale farmers in Alnajaf province to continue hiring various agricultural machinery services rather than purchase them due to the absence of safety margin of holding areas. Therefore machine stations of agricultural machineries services maybe developed by Iraqi government in the study region to provide these services to the farmers with supported prices.

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21

Roman, Monika. "Polski handel zagraniczny środkami mechanizacji rolnictwa w latach 2004-2015." Zeszyty Naukowe SGGW w Warszawie - Problemy Rolnictwa Światowego 17(32), no. 1 (March 30, 2017): 161–72. http://dx.doi.org/10.22630/prs.2017.17.1.15.

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The aim of the article was to determine the changes in foreign trade of farm machinery in Poland in 2004-2015. The article presents the value of exports and imports of farm machinery in total, according to geographical area, as well as for selected types of agricultural machinery. The article uses secondary data from the Ministry of Finance, gathered as part of the Integrated Customs Tariff Information System. In the analyzed period, Poland was characterized by a negative balance of foreign trade. The value of imports of farm machinery increased more than threefold, while the value of exports nearly quadrupled. In 2015, Poland exported most of its farm machinery to the EU-28 (65% of export value), especially to Germany, France and Belgium. Within the EU-28, Poland is also the most imported (87% of import value), and its main trade partners were Germany, Italy and France.

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22

Cupiał, Michał, and Zbigniew Kowalczyk. "Optimization of Selection of the Machinery Park in Sustainable Agriculture." Sustainability 12, no. 4 (February 13, 2020): 1380. http://dx.doi.org/10.3390/su12041380.

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A correct selection of the machinery park is vital for correct functioning of a farm. It is significant in sustainable farming where, except for economic factors, application of a suitable technique and technology in order to lead an effective production is crucial. The paper presents a method of designing a set of machines for a farm. The method was implemented in the computer application. A suitably selected, optimised machinery park enables a sustainable agricultural production and achievement of the desired economic, production and environmental effects. The application was practically verified and is used in teaching and farming practice.

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23

Edet, Uduak, and Danny Mann. "Remote supervision of autonomous agricultural machines: Concepts and feasibility." Canadian Biosystems Engineering 62, no. 1 (January 25, 2022): 2.1–2.12. http://dx.doi.org/10.7451/cbe.2020.62.2.1.

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The scientific literature provides a description of various models depicting autonomous agricultural machines working to complete typical field operations. Many of the models involve some form of automation interface that is used by the machine owner to supervise the operation of the machine from a remote location. The objective of this study was to interview experts in the design of autonomous agricultural machines (university researchers, entrepreneurs, and leaders in the agricultural machinery sector) to ascertain their opinions about future autonomous agricultural machines, particularly related to how such machines will be supervised by the machine’s owner. Of the four remote supervision concepts described by participants (within the field, close to the field, from the farm office, and outside the farm site), the close-to-the-field remote supervision concept was determined to be the most viable concept. Designers were divided on the idea of providing real-time live video on the automation interface, however, most of them believed that having live video would reassure the farmer that everything was going well. Desktop computer, tablet and phone were the main devices recommended as tools for remote supervision (i.e., the hardware on which to display the automation interface), with tablet perhaps being the preferred alternative.

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24

Rudenko, N. Ye, S. P. Gorbachev, and K. D. Padaltsin. "Tensometric device for dynamometry of mounted agricultural machinery." Traktory i sel hozmashiny 82, no. 1 (January 15, 2015): 23–24. http://dx.doi.org/10.17816/0321-4443-65459.

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A tensometric device is developed. It allows to determine tractive resistance of mounted agricultural machinery in farm conditions in order to select a power unit and determine the optimal grasp width of machine for specific soil and climate conditions.

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25

Hornowski, Andrzej, and Tomasz Kondraszuk. "USE METHODOLOGY OF BEP TO ANALYZE MECHANIZATION COSTS ON FARM." Annals of the Polish Association of Agricultural and Agribusiness Economists XIX, no. 3 (August 22, 2017): 85–91. http://dx.doi.org/10.5604/01.3001.0010.3364.

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The article attempts to adapt the BEP analysis methodology to assess the viability of agricultural machinery. It was assumed to treat the machine depreciation cost as a fixed cost, but only to the rational use threshold. Above this threshold, it was considered that depreciation should be calculated using the active method and treated as a variable cost. In this case it’s a technical potential and its wear becomes a bottleneck. In addition, it was considered reasonable to take into account the cost of interest on the capital employed, which would be a fixed cost. So far research on the efficiency of machinery utilization has focused on the quantitative analysis of their use, and the financial aspects are ignored. The proposed use of the BEP methodology allows not only quantitative and qualitative yield thresholds for the analyzed machine, but also the calculation of the limit values of the variable component costs (fuel prices, repairs) and fixed costs (garage, maintenance, interest rates).

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26

Islam, AKMS, and MJ Kabir. "Rental Service Market of Farm Machinery in Rice Cultivation: A Farm Level Investigation." Bangladesh Rice Journal 21, no. 1 (July 12, 2018): 35–45. http://dx.doi.org/10.3329/brj.v21i1.37376.

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This study was undertaken to investigate the rental charge and labour cost of five major operations in rice production in the north-west region of Bangladesh. Farmers’ group discussions (FGDs) with 10-15 key informants farmers were conducted for collecting farm level data from 15 villages of both Rajshahi and Rangpur divisions. Rental charge and labour cost of rice cultivation were converted to paddy equivalent cost (PEC) for tillage, transplanting, weeding, harvesting, carrying and threshing. Farmers mostly relied on the service providers of either power tiller or tractor for tillage. They also relied on the service providers of close drum power thresher for threshing rice and other crops. However, farmers still manually carried out transplanting, weeding, harvesting and carrying of rice. Both the contractual and daily basis hired labour and family labour performed those activities. The wages of labour of harvesting and carrying was paid in cash or kind (e.g., share of harvested grain or fixed amount of grain per unit of harvest area). The average paddy equivalent cost for the five major operations was ranged between 37-47% of total paddy production cost in the study locations. The mechanized transplanting and harvesting may reduce rice production cost largely as well as would resolve the labour scarcity problems. Therefore, transplanter and harvester have the great prospect of widespread adoption. However, prospect of mechanical weeding is limited because of the availability of low cost intensive chemical control methods. Thus, providing the larger financial assistance on purchasing both the transplanter and harvester facilitates the end users to enhance the productivity and reduce the cost of rice farming.Bangladesh Rice j. 2017, 21(1): 35-45

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27

Pudup, Mary Beth. "From Farm to Factory: Structuring and Location of the U.S. Farm Machinery Industry." Economic Geography 63, no. 3 (July 1987): 203. http://dx.doi.org/10.2307/143950.

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28

Robb, James G., John A. Smith, and Daryl E. Ellis. "Estimating Field Machinery Cost: A Whole Farm Approach." Journal of Natural Resources and Life Sciences Education 27, no. 1 (1998): 25–29. http://dx.doi.org/10.2134/jnrlse.1998.0025.

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29

R. S. Parmar, R. W. McClendon, and W. D. Potter. "Farm Machinery Selection Using Simulation and Genetic Algorithms." Transactions of the ASAE 39, no. 5 (1996): 1905–9. http://dx.doi.org/10.13031/2013.27669.

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30

Copithorne, L. W. "THE PATTERN OF INTERNATIONAL TRADE IN FARM MACHINERY*." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 22, no. 1 (November 13, 2008): 42–57. http://dx.doi.org/10.1111/j.1744-7976.1974.tb00917.x.

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31

W. C. Hammond, B. L. Tyson, and F. W. Kay. "Testing an Emergency Stop Concept for Farm Machinery." Applied Engineering in Agriculture 1, no. 1 (1985): 13–15. http://dx.doi.org/10.13031/2013.26755.

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32

Soliman, A. M. I., and A. E. Khater. "COMPUTER MODEL FOR SUPPORTING FARM MACHINERY REPLACEMENT DECISION." Journal of Soil Sciences and Agricultural Engineering 5, no. 9 (September 1, 2014): 1221–32. http://dx.doi.org/10.21608/jssae.2014.49654.

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33

Reid, Donald W., and Garnett L. Bradford. "A Farm Firm Model of Machinery Investment Decisions." American Journal of Agricultural Economics 69, no. 1 (February 1987): 64–77. http://dx.doi.org/10.2307/1241307.

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34

Kaivosoja, J., and R. Linkolehto. "GNSS error simulator for farm machinery navigation development." Computers and Electronics in Agriculture 119 (November 2015): 166–77. http://dx.doi.org/10.1016/j.compag.2015.10.021.

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35

B, Kailashkumar, SS Sivakumar, J. John Gunasekar, PK Padmanathan, V. Alex Albert, and R. Ravikumar. "Nanotechnology in farm machinery: New opportunities and perspectives." Pharma Innovation 11, no. 2S (February 1, 2022): 1293–97. http://dx.doi.org/10.22271/tpi.2022.v11.i2sq.10945.

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36

Kim, Suhkhyun, Gangcheol Seo, and Changyong Kang. "Cost Minimizing Farm Machinery Selection for Rice Farming." Korean Agricultural Economics Association 62, no. 4 (December 30, 2021): 27–51. http://dx.doi.org/10.24997/kjae.2021.62.4.27.

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37

Purwantoro, Deddy, Trikuntari Dianpratiwi, and Sri Markumningsih. "Analisis Penggunaan Alat Mesin Pertanian Berbasis Traktor Tangan pada Kegiatan Perawatan Budidaya Tebu." agriTECH 38, no. 3 (March 12, 2019): 313. http://dx.doi.org/10.22146/agritech.28149.

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Java contributes 63.7% of national sugar production, in which 55.6% is produced by the farmer from 312,973 ha of sugarcane area (65.6% of Indonesia’s cane crop area). Farm labor in Java was very limited in the past two decades, so was needed mechanization. Howeveer, the size of agricultural machines were not accordance with the size of land, the limited of spares, and was not optimally managed. This research aimed to calculate operational cost of farm machinery (hand tractor) on the cane cultivation activities up to 6-month cane growth period. This study was carried out at Comal, Center of Java at alluvial land 8 mdpl and at Pasuruan, East Java at alluvial 10 mdpl. From each location, 2 plots of 0.1 ha were observed. The treatments were manual and machine cultivation with 3 replications. The observed agrotechnical parameters were the bud emergence, number of stalk, clump, stalk height and diameter, and operational cost of using machinery. The agrotechnical result using t-test (95% confidence rate) showed no significance among treatments. Other results revealed that using machinery for fertilizing, soil heaping up, and soil moving (gulud) in Pasuruan can substitute manual daily worker (HOK) for 6 HOK, 12 HOK and 20 HOK, respectively, using tractor by 7 hours/day. Meanwhile in Comal, using machinery for fertilizing, soil heaping up, and soil hilling up (gulud) can substitute manual daily worker (HOK) for 9 HOK, 16 HOK, and 20 HOK, respectively. The cost analysis indicated the 35.54% reduction of using machinery compared to the manual expense. This study revealed that the machinery could substitute the manual worker for cane cultivation.

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FURNISS, I. F. "Replacement Costs of Farm Machinery on Illustration Station Farms in the Prairie Provinces." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 5, no. 2 (November 13, 2008): 9–14. http://dx.doi.org/10.1111/j.1744-7976.1957.tb01285.x.

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Wilson, John, Ida Harper Simpson, and Richard Landerman. "Status Variation on Family Farms: Effects of Crop, Machinery, and Off-farm Work1." Rural Sociology 59, no. 1 (February 3, 2010): 136–53. http://dx.doi.org/10.1111/j.1549-0831.1994.tb00526.x.

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Søgaard, Henning T., and Claus G. Sørensen. "A Model for Optimal Selection of Machinery Sizes within the Farm Machinery System." Biosystems Engineering 89, no. 1 (September 2004): 13–28. http://dx.doi.org/10.1016/j.biosystemseng.2004.05.004.

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Rajkhowa, Pallavi, and Zaneta Kubik. "Revisiting the relationship between farm mechanization and labour requirement in India." Indian Economic Review 56, no. 2 (October 18, 2021): 487–513. http://dx.doi.org/10.1007/s41775-021-00120-x.

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AbstractIn many developing and emerging economies, better employment opportunities in the non-farm sector have increased rural wages due to labour shortages during the peak agricultural season. Increasing wages often cause a substitution of labour for mechanical power, but extensive use of labour-saving technologies may cause labour displacement and have serious equity concerns. Using the household and individual fixed effect estimation approach, this paper analyses the relationship between different types of farm machines and labour requirements in India. The results suggest that a unit increase in the level of farm mechanization increases the demand for hired labour by 12%. Moreover, we find that the level of farm mechanization has a positive effect on women’s participation in farm work, while it decreases the probability of children participating in agriculture-related work. Disaggregated analysis based on types of farm machinery suggests that water-lifting equipment, draft power and tractors increase the probability of male household members working on their farms, while all types of farm machines, except tractors, have a positive effect on female farm labour participation. We also find that the effect of farm mechanization on the demand for hired labour decreases as the size of the farm increases.

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Xu, Yu, Liangjie Xin, Xiubin Li, Minghong Tan, and Yahui Wang. "Exploring a Moderate Operation Scale in China’s Grain Production: A Perspective on the Costs of Machinery Services." Sustainability 11, no. 8 (April 12, 2019): 2213. http://dx.doi.org/10.3390/su11082213.

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Due to the indivisibility of agricultural machinery in production, limited farm sizes have reduced mechanization efficiency and increased the cost of grain production in China. As a result, the development of a moderate-scale farming system has been proposed by academic communities and policy makers. However, it is still hotly debated how to determine a moderate farm scale. We offer a new perspective on the costs of machinery services. This manuscript employs the threshold model and uses the nationally representative data from the 2015 China Rural Household Panel Survey (CRHPS) to investigate the role of farm size expansion relative to per area machinery services expenditures (PAMSE). The empirical results reveal that there is a nonlinear relation between the farm size and PAMSE. Specifically, farm size expansion can reduce the PAMSE by improving mechanization efficiency in all cases, while the magnitude of cost-saving is progressively reduced in the process of farm scale expansion. In particular, a 1 mu (1 mu = 1/15 ha) increase in the farm scale could only lead to a 0.3% decrease in the PAMSE when the farm size exceeded 50 mu, which indicates that 50 mu is a minimum efficient farm scale to achieve most economies of scale. Therefore, we suggest that persistent efforts should be devoted to improving farmland circulation efficiency and developing scale farms. More importantly, governmental supporting policies, such as agricultural subsidies, need to attach more importance to these large farms.

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Sarkar, Anupam. "Agricultural Mechanization in India: A Study on the Ownership and Investment in Farm Machinery by Cultivator Households across Agro-ecological Regions." Millennial Asia 11, no. 2 (August 2020): 160–86. http://dx.doi.org/10.1177/0976399620925440.

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Despite the signs of distress and fatigue in Indian agriculture, the use of machines in crop operations has increased manifold in recent times. Proponents of mechanization argue that mechanization saves time and cost, and improves agricultural productivity. The Government of India has also encouraged mechanization through subsiding farm machinery and establishing custom hiring centres. Earlier studies have found significant regional variation in the spread of agricultural mechanization. Cost of cultivation data collected by Commission for Agricultural Costs and Prices (CACP) reveal that although the share of machine labour in the cost of cultivation per hectare has increased, there exist significant differences across crops in the use of mechanical input. Empirical studies in different regions have found a significant presence of the rental market for services of agricultural machinery. However, in recent years there have been very few studies on the patterns of mechanization based on household-level data on ownership and investment in agricultural machinery and implements. Agro-ecological characteristics of a region are said to have a significant impact on the level of mechanization. Ownership of machinery is also significantly influenced by the factors such as size of landholding, access to irrigation and access to institutional credit. The pattern of investment and ownership of machinery has important implications on the profitability of farming. This article uses unit-level data from the All-India Debt and Investment Survey (NSS 70th round 2013) to study the pattern of agricultural mechanization in India based on the ownership of agricultural machinery and implements. Using ownership and expenditure data it studies the questions of recent patterns in agricultural mechanization across agro-ecological regions and land classes.

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PARK, Won KYU. "A study on the utilization pattern of farm machinery and farm mechanization in Korea." Japanese Journal of Farm Work Research 25, no. 3 (1990): 275–81. http://dx.doi.org/10.4035/jsfwr.25.275.

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Kingston, David C., Behzad Bashiri, Abisola Omoniyi, and Catherine M. Trask. "Body Orientation and Points of Contact during Laboratory-Based Machinery Egress: Investigating Adherence to Safety Guidelines." Journal of Agricultural Safety and Health 26, no. 3 (2020): 95–104. http://dx.doi.org/10.13031/jash.13931.

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HighlightsMost of the experienced operators performed machine egress facing out from the cab.Egress was 2.5 s longer when facing in toward the machine, but no differences were observed in points of contact maintained when compared to egress facing out.Maintaining at least three points of contact during egress was observed for only approximately 30% of egress duration.A one-hour exposure to whole-body vibration did not change points of contact behavior nor egress duration when performing egress while facing out.Abstract. Mobile farm machinery operators are at a high risk of injury when entering (ingress) and exiting (egress) the cabs of such machinery due to slips and falls. Safety organizations and equipment manufacturers have delivered a consistent message: operators are to egress machines facing in, toward the access path, and maintain three points of contact at all times. This study used a laboratory-based model of a mid-sized agricultural tractor to determine adherence to best practices for safety and the effect of acute whole-body vibration exposure on compliance. The majority of 19 experienced operators (16 male, 3 female) performed machinery egress facing out from the cab because descending while facing in toward the machine took 2.5 s longer. Maintaining at least three points of contact during egress was observed for only approximately 30% of egress duration, but was as high as approximately 41% for participants who self-selected the facing-in orientation. Exposure to 1 h of whole-body vibration did not change points of contact behavior nor trial duration when performing egress while facing out. Overall, the model cab used in this study had safety features similar to a real-world machine, indicating that there may be opportunities in access path or cab door design to promote increased points of contact use. Future work is needed to accurately assess three-dimensional movement patterns and external forces for disease and injury risk models. Keywords: Access path, Farm machinery, Safety guidelines, Occupational injury.

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Prabhavathi, Y., N. T. Krishna Kishore, and Ch Charishma. "Penetration of farm mechanization services through FPOs: rural dynamics-based business modelling." INTERNATIONAL JOURNAL OF AGRICULTURAL SCIENCES 17, no. 2 (June 15, 2021): 558–68. http://dx.doi.org/10.15740/has/ijas/17.2/558-568.

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Farm mechanisation although one among the essential input to raise the agriculture productivity, but individual owning of agricultural machinery by resource constrained small and marginal farmers who constitute around 85% of operated land holdings in India is uneconomical. Hence, innovative arrangements such as custom hiring centres’ (CHCs) are being encouraged through farm aggregation models like cooperative farming, Joint Liability Groups (JLG), Farmer Producer Organizations (FPOs) to get access to farm machinery services at affordable prices and promote mechanization of operations on small farms. With this background, the present study is taken upto assess the feasibility for the establishment of FPO owned and operated model custom hiring centre (CHC) in Nimmanapalle mandal of chittoor district of Andhra Pradesh state and formulate suitable business strategies for ensuring viability of the unit. The sample size of the study was 120 farmers. The major crops grown in the study area are tomato, paddy and groundnut and the market potential for farm machinery is estimated at Rs. 269.73 lakhs. The SWOT analysis conducted indicated the opportunity for establishment of CHC due to inadequate farm machinery services, labour shortages and farmers habituated to hiring services. The financial assessment for the proposed unit over a five year period showed that the unit is worth investing as reflected by positive NPV of 6.56 lakhs at 12% discount rate, BCR of 1.05 and IRR of 17.27%. The debt service coverage ratios of greater than two from second year onwards and annual increase of positive cash accruals signifies the unit strength in meeting the debt obligations. The unit if established shall have long term social benefits that includes increase in input use efficiency of farm resources due to timeliness of operations, productivity, yields, income levels in addition to creation of employment in non-farm sector.

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Ilin, V. A. "AUGMENTED REALITY TECHNILOGY IN TECHNICAL SERVICE OF FARM MACHINERY." VESTNIK OF THE BASHKIR STATE AGRARIAN UNIVERSITY 47, no. 3 (September 23, 2018): 40–43. http://dx.doi.org/10.31563/1684-7628-2018-47-3-40-43.

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Khatawkar, Dipak S., P. Shaji James, and D. Dhalin. "Modern Trends in Farm Machinery-Electric Drives: A Review." International Journal of Current Microbiology and Applied Sciences 8, no. 1 (January 10, 2019): 83–98. http://dx.doi.org/10.20546/ijcmas.2019.801.011.

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Shamshiri, Ramin, and Wan Ishak Wan Ismail. "Exploring GPS Data for Operational Analysis of Farm Machinery." Research Journal of Applied Sciences, Engineering and Technology 5, no. 12 (April 10, 2013): 3281–86. http://dx.doi.org/10.19026/rjaset.5.4568.

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., A. K. M. Saiful Islam, M. A. S. Kowser Sarker ., M. A. Rahman ., M. M. Hossain ., and M. M. Alam . "Production Problems of Farm Machinery Manufacturing Industry in Bangladesh." Journal of Biological Sciences 1, no. 10 (September 15, 2001): 955–59. http://dx.doi.org/10.3923/jbs.2001.955.959.

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