scholarly journals Nutrients influence seasonal metabolic patterns and total productivity of Arctic streams

2020 ◽  
Author(s):  
Maria Myrstener ◽  
Lluís Gómez‐Gener ◽  
Gerard Rocher‐Ros ◽  
Reiner Giesler ◽  
Ryan A. Sponseller
Keyword(s):  
Total Productivity ◽  
Arctic Streams

scholarly journals Maize intercropping in the milpa system. Diversity, extent and importance for nutritional security in the Western Highlands of Guatemala

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Santiago Lopez-Ridaura ◽  
Luis Barba-Escoto ◽  
Cristian A. Reyna-Ramirez ◽  
Carlos Sum ◽  
Natalia Palacios-Rojas ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Farm Households ◽  
Small Scale ◽  
Agricultural System ◽  
Energy Yield ◽  
Common Beans ◽  
Nutritional Security ◽  
Nutrient Adequacy ◽  
Faba Beans ◽  
Total Productivity

AbstractWe present an assessment of the extent, diversity, and nutritional contribution of the milpa through a quantitative analysis of data from a survey conducted in 989 small scale farm households in the Western Highlands of Guatemala (WHG). The milpa is a traditional agricultural system in which maize is intercropped with other species, such as common beans, faba beans, squashes or potatoes. Our study shows that more than two-thirds of the 1,205 plots recorded were under the milpa system, with a great diversity of crop combinations. As shown with the 357 plots for which specific yields were available, milpa systems present higher total productivity than monocropped maize, expressed as total energy yield of the harvested crops in the respective system, and were also better at providing the recommended daily allowances of fourteen essential nutrients, based on a Potential Nutrient Adequacy (PNA) indicator. Maize-bean-potato, maize-potato, and maize-bean-faba intercrops had the highest PNAs, and monocropped maize, the lowest. These results support the implementation of milpa systems tailored to different agro-ecologies in order to improve nutrition in the WHG and a variety of similar regions.

Ecology of Arctic Streams and Rivers

2020 ◽  
pp. 181-218
Author(s):  
Alexander D. Huryn
Keyword(s):  
Streams And Rivers ◽  
Arctic Streams

scholarly journals Analisis Sistem Rantai Pasok PT. Semen Padang

2011 ◽  
Vol 10 (1) ◽  
pp. 113
Author(s):  
Fitri Julasmasari
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Performance Measurement ◽  
Material Flow ◽  
Supply Chain Performance ◽  
Chain Management ◽  
Organizational Goals ◽  
Total Productivity ◽  
Corporate Profits ◽  
The Relationship

Supply Chain Management is a concept or mechanism to increase the total productivity of companiesin the supply chain through optimization of the time, location and quantity of material flow from rawmaterials into semi-finished products and finished products. Supply Chain Management is not onlyabout the relationship with the supplier but also the link between the company and consumers. Tofind out the value of the company supply chain performance within a certain time, we need a draftmodel of supply chain performance measurement in which the design will be implemented at thecompany. By knowing the value of supply kunerja chainnya the company can improve theeffectiveness and productivity of the organization to achieve organizational goals is to win thecompetition, and increase corporate profits. Kata kunci: Supply Chain Management, PT. Semen Padang

scholarly journals In what direction is agricultural specialization headed in Central and Eastern Europe? (2005-2016)

2020 ◽  
Author(s):  
Csaba Forgacs ◽  
Keyword(s):  
Growth Rate ◽  
Labour Productivity ◽  
Farm Size ◽  
Farm Production ◽  
Input Side ◽  
Leading Position ◽  
Total Productivity ◽  
Ranking Order ◽  
Eastern European Countries ◽  
Cattle Rearing

The paper deals with the main directions of specialization in Central and Eastern European Countries’ (CEECs, EU10) agriculture after the EU’s Eastward Enlargement. We analyse and compare growth and productivity advantages of specialized farm types by physical size (in hectare) in EU10 member-states to the EU10/27/15 average based on EUROSTAT data in the period of 2005-2016. We focus on exploring the main directions of specialization using such indicators as the number of specialized farms, land (Utilized Agricultural Area, UAA) and labour (Agricultural Working Unit, AWU) use on the input side, average farm size by land and labour use as well as area-, labour-, and total productivity on the output side. We conclude that the directions of specialization in farming in EU10 were based on the traditional farm production structure making the latter better able to adjust and take advantages of Common Agricultural Policy. Concerning production growth rate, the three leading specialization types of CEECs’ farms were: (i) cattle rearing and fattening, (ii) cereals, oilseed and protein crops and (iii) fruits and citrus fruits. These three specialization types of farms – in the same ranking order - also increased land (UAA) and labour (AWU) use well above the average. Specialized cereals farms and cattle rearing and fattening farms were also ranked in top three by number. Both cereals and fruits specialized farms have leading position in growth rate of land and labour use and also are in top three in growth rate of land area and total farm productivity. Cereals and fruits specialized farms also more than doubled labour productivity during the first decade after the EU’s Eastward enlargement but did not rank in the top three in this category.

scholarly journals Macroeconomic Dynamics of the Total Factor Productivity of the Russian Economy

2021 ◽  
Vol 17 (3) ◽  
pp. 799-813
Author(s):  
Sergey A. Mitsek
Keyword(s):  
Total Factor Productivity ◽  
Public Investment ◽  
Labour Productivity ◽  
Aggregate Demand ◽  
Factor Productivity ◽  
Export Prices ◽  
Russian Economy ◽  
Economic Trends ◽  
Total Productivity ◽  
Economic Growth Rates

The growth rate of Russia’s total productivity has been slowing down significantly since 2008. The majority of relevant publications either describe an economic methodology or specifically focus on labour productivity. However, economic growth rates, as well as community welfare, largely depend on total factor productivity. The paper aims to determine the reasons for the slowdown in the growth of total factor productivity after 2008. This negative dynamics was assessed using a macroeconomic econometric model and estimates for Russian regions and types of economic activity. Elasticity of dependent variables was calculated based on econometric equations as well as multipliers of exogenous variables presented in the model. Ordinary and rank correlations between the variables were also examined. The calculations revealed that the stagnation of total factor productivity was caused by the misallocation of resources across industries and regions, de crease in aggregate demand, increase in capital goods prices (primarily due to rouble devaluation) and a slowdown in digital economy development. In turn, these trends were influenced by a decline in public investment and export prices, as well as a slowdown in population growth and liquidity. Simultaneously, growth of the world economy contributed to the demand for Russian export goods, preventing a decrease in productivity. The findings can be used for forecasting Russian economic trends and developing relevant policy measures. Further research will examine the role of human capital, energy intensity, climate and institutional factors in increasing the total productivity.

scholarly journals TOTAL FACTOR PRODUCTIVITY AND FRONTIER PRODUCTION FUNCTION = PRODUKTIVITAS FAKTOR PRODUKSI TOTAL DAN FUNGSI PERODUKSI FRONTIER

Agro Ekonomi ◽  
2016 ◽  
Vol 24 (2) ◽  
pp. 2
Author(s):  
Sri Widodo
Keyword(s):  
Total Factor Productivity ◽  
Production Function ◽  
Productivity Growth ◽  
Factor Productivity ◽  
Single Factor ◽  
Cross Sectional ◽  
Potential Output ◽  
Total Productivity ◽  
Cross Sectional Studies ◽  
Frontier Production Function

The total factor productivity became an interesting concept in the measurement of productivity growth. Productivity is a ratio of output to input. The most common measurement of productivity is single factor productivity or partial productivity such as of land, labor, or capital.A total (factor) productivity is a productivity of all factors of production where the factors are aggregated. In cross-sectional studies this total productivity is a ratio of actual to potential output where the potential output is estimated from ther frontier production function. One of the methods to estimate this frontier function is by using linear programming technique.The total productivity does not always coincide with a single factor productivity of land (yield), that in the study area the larger farms tend to have higher total productivity than yield

scholarly journals Analysis Productivity of Palm Oil Processing with Marvin E. Mundel Method

2019 ◽  
Vol 2 (2) ◽  
pp. 41-48
Author(s):  
Yuza Elfa Lestari ◽  
Ahmad Rifai ◽  
Didi Muwardi
Keyword(s):  
Palm Oil ◽  
Measurement Method ◽  
Productivity Index ◽  
Productivity Measurement ◽  
Oil Processing ◽  
Area 7 ◽  
Total Productivity ◽  
Work Shifts ◽  
Farmer Groups

This research aims to know the productivity of palm oil processing, evaluated the productivity and give solutions  improved productivity of palm oil processing in PTPN V Sungai Pagar district of Kampar Kiri Kampar Regency. The productivity measurement method used is the Marvin E. Mundel method. Measurement of productivity by setting the month of January 2016 as the basis period of the partial results obtained the highest productivity index of 160.13% February 2017 for depreciation, amounting to 132.16% July 2016 for materials, amounting to 258.26% January 2017 for labor, of 273.12% January 2017 for energy, amounted to 263.04% and September 2016 for maintenance.While the lowest partial productivity index of 93.93% December 2017 for depreciation, amounting to 94.07% February 2017 for the material, to 65.00% June 2016 for labor, of 66.25%, June 2016 for energy, and of 67.43% April 2016 for maintenance.  The highest total productivity index was reached in February, 2017 of 133.70% and lowest in February 2016 of 94.24%.  Solutions for increasing the productivity of palm oil processing: 1) increasing the number of FFB processed by paying attention to the quality of the fruit to be received 2) improving control of employee work, 3) making service schedules for PKS machines and equipment, 4) processing activities carried out in accordance with the maximum hours of processing PKS, 5) repair of production floor and loading ramp, 6) hold socialization in KUD/farmer groups and communities around PKS area, 7) improve company relations with partners, and 8) make improvements to the division of work shifts.

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