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Logistics in agricultural production

Logistics in agricultural production

3. Calculation of the volume of material handling in the enterprise (which will be higher than the volume of material flow, because of two or three handling opera- tions with some materials). We recommend to consider as one handling operation one loading and one un- loading of the same material, with no regard to the way of realization. But this realization must be carried out by own farm workers only, not by the means of any service organization. The volume of handling opera- tions can be influenced by the management much more than the volume of the material flow. First of all, it is necessary to find out materials which demand the larg- est volume of these jobs and to judge especially care- fully the volume of manual handling operations. In this direction, the rationalization activities must be focused. 4. The volume of handling operations in non-agricultural production. Calculation of this volume will depend on the specific activities and planned production per year. 5. Calculation of necessary machines for transport and material handling. This calculation should link to the previous data and should show the critical points which will be necessary to improve by the means of changing the technology, new machines or outsourcing (using service organization).
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Technical Efficiency of Agricultural Production in Ethiopia

Technical Efficiency of Agricultural Production in Ethiopia

Farmers faced low productivity due to lack of knowledge on maximizing level of output at the given level of inputs. Technical efficiency of agricultural production in the Ethiopia were assessed by using cross-sectional secondary data collected from Ethiopia socioeconomic survey in 2015/16 production year. Cobb-Douglas stochastic frontier production function model was used to estimate technology and determinants of technical inefficiency simultaneously using the maximum likelihood estimator (MLN). MLN estimation results showed that increasing input use like area, seed, oxen, fertilizer and labor would increase yield of agricultural production. The coefficients of elasticity for area, seed, oxen, fertilizer and labor were 0.21, 0.29, 0.38, 0.12 and 0.10 respectively. Consequently, agricultural production exhibits increasing return to scale because the sums of input elasticity’s were greater than one which is 1.1. The mean technical efficiency of farmers in the agricultural production was about 36%. The implication is that there is an opportunity to increase output on average by 64% through efficient use of inputs given the current input use and technology. The discrepancy ratio gamma (γ) which measures the relative deviation of output from the frontier due to inefficiency was about 86 percent indicating that about 86% of variation in agricultural production among the farmers was attributed to technical inefficiency effects. Thus, it is possible to improve technical efficiency through better use of these factors.
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Institutional credit and agricultural production nexus

Institutional credit and agricultural production nexus

Now we explore the thing that there is a long run relationship among said variables. The next step is to find the short run dynamics among the variables. Error correction mechanism is used for short run dynamics, in which error correction term shows the speed of convergence towards equilibrium. It is significant and negative in sign. The speed of correction towards equilibrium depends upon the value of error correction term. Big value shows the slow speed of adjustment towards equilibrium and vice versa. Most of the variables are statistically insignificant, except agricultural labor force and error correction term. Negative sign of agricultural labor force depicts that as the agricultural labor force increases the agricultural production decreases. Durbin-Watson statistic shows that the model has no autocorrelation while, F-statistic pointed the good fit of the model.
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Impact of water management on agricultural production

Impact of water management on agricultural production

This paper explores the impact of water management on output, labor, and capital growth for an agriculture-based developing economy like Pakistan. According to empirical findings, capital stock and labor force in the agriculture sector significantly affect output growth. Improvement of capital stock in the form of mechanization, improved seeds, fertilizers and pesticides on one hand, and labor force skills, techniques and management, on the other hand, will bring positive and significant impact on agricultural output. Any improvement in policy management by authorities will enhance agricultural production manifold. Water at farm gate, tube wells, and access to credit of farmers increase agricultural output by enhancing the productivity of capital and labor. Proper water management will result in efficient allocation of resources and has an indirect positive impact on growth of output. To increase overall efficiency, an irrigation technology that efficiently uses water for intensive crop production must be developed.
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Synergistic effect in the management of agricultural production
 

Synergistic effect in the management of agricultural production  

Abstract: Generally, when a certain type of agricultural production is optimized, an interaction is said to exist between two factors when the response (the yield of agricultural production) at the diff erent levels of one factor is aff ected by the level of the other factor present. Th us, the combined eff ect of the two factors is not simply the sum of their separate eff ects, but either more than this sum (synergistic interaction) or less than this sum (antagonistic interaction). A 3 factor interaction between the factor f 1 , f 2 , and f 3 occurs when the nature of the interaction between f 1 and f 2 is diff erent at the various levels of f 3 . Higher order interactions can be similarly defi ned but these become increasingly more diffi cult to interpret. A disa- dvantage of this approach is that it explores each of the agricultural sub-process unrelated to the follow-up sub-process (e.g., this approach optimizes the process of fertilization without links to the crops distribution process and unrelated to the price optimization). Th is is because each sub-process has a diff erent response unit that is the subject of optimization. Th erefore, it is appropriate to investigate the interaction of agricultural production in its integrated form, through a univer- sal response of variables (e.g., in the form of utility from the response). Th is new approach is introduced in the paper. Th us, the objective of the paper is to present a new method for the formal determination of synergistic eff ects of the agricultural enterprises management.
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The Change Path of Agricultural Production Outsourcing

The Change Path of Agricultural Production Outsourcing

China’s National Bureau of Statistics reported that the number of Chinese migrant workers reached 252780000 in 2011, which increased by 4.4% on the basis of 2011. Due to various reasons, these migrant workers are not willing to give up the right to their contracted land, though in reality they are unable & unwilling to work on the land. Therefore, some places, particularly in coastal area, farming is abandoned or roughly managed. In order to increase the land use efficiency and deal with the living cost increase caused by food price increase, there are special individuals or organizations in the countryside to provide the service from planting to harvesting during the agricultural production. Therefore the migrant workers can sub-contract their land to others. This is the agricultural outsourcing system taking shape from the existing rural operation system, the social security system and the urbanization of migrant workers. The general outsourcing service emerged in Adam Smith’s age. International outsourcing service also existed from an early age, for example, the traditional international logistics industry. The modern outsourcing service started from 1980s, the theoretic icon was the theory put forward by Prof. Drucker, the famous managerialist, that in any enterprise, any work that only supplies backup support & does not create direct production value should be outsourced. In China, the outsourcing service started early with different technical contents and levels, covering the high low-end services. In 1980s and 1990s, with the wave of IT industry, research on outsourcing also increased, including the significance of outsourcing, economic analysis, developing characteristics, and affecting factors, outsourcing system, organizational management and specific empirical analysis. Generally speaking, outsourcing service reduces the production cost and increases the profit margin by taking advantage of specialization of work, resource endowment, economies of
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Mechanization of agricultural production in developing countries

Mechanization of agricultural production in developing countries

Land degradation has been reported as one of the challenges in the mechanisation of agricultural fields (Fonteh, 2010). This problem can be found in ‘hotspots’ region of Asia such as the foothills of the Himalayas, sloping areas in Southern China, South-East Asia and the Andes, forest margins in East Asia and the Amazon, rangelands in Africa and West and Central Asia, and in the Sahel. Such places have concentrations of rural poor, often as ethnic minorities. Improved land management technologies can thus be applied to maintain the quality of the natural resources which is often needed in mechanised agricultural production. Examples include: range management to reduce overgrazing; soil organic matter restoration through composts; animal-crop rotational grazing; crop rotation; agroforestry and fallowing systems; land reclamation; and earth or vegetative bunds against erosion.
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The Role of Information Technology on Agricultural Production in Nigeria

The Role of Information Technology on Agricultural Production in Nigeria

Abstract-Effective communication of agricultural information to farmers is crucial in achieving optimum efficiency in agricultural production in Nigeria. IT can help an average Indian farmer to get relevant information regarding agro-inputs, crop production technologies, agro processing, market support, agro-finance and management of farm agri-business, IT in this information age has been recognized as an essential medium of disseminating information and advice to farmers. In Nigeria today, the concept of ITs has become a global concern and the increasing application of the technology in every segment of our natural life, especially through the GSM, radio, television, projectors, internet, video, camera, computers, has been felt. The paper relies on primary source of information and heavily on literature. It highlights the emerging role of ITs in agricultural production in Nigeria and suggests areas of improvement in fulfilling the roles. ITs play numerous key roles in Agricultural production such as: helping in expanding outreach to a large number of farmers; offering a solution to resource and capacity issues within the agricultural sector; improving information flow and connecting people within the rural areas, the challenges identified include poor IT infrastructural development, high charges for radio/television presentations, inadequate capital for farmers and insufficient knowledge of ITs. It is recommended that farmers be encouraged to access and utilize ITs by supporting them financially and materially. Also more awareness creation and training of both farmers and extension agents on the effective and efficient use of IT facilities be intensified by the governments.
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Agricultural Production and Land Use Season 2000A

Agricultural Production and Land Use Season 2000A

The second section of table 14 suggests, however, that the post-war changes made in data collection methods probably introduced a substantial amount of non-sampling error in several areas. In the interest of creating an agricultural statistical system that would be manageable in terms of cost and sustainable in the future, simplified data collection methodologies were developed for measuring agricultural production and land use. While these new methodologies have proven to be far less costly and easier to implement than the former system, it is believed that the precision of the estimates from these data has been reduced.
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Risk of agricultural production in russian orel region

Risk of agricultural production in russian orel region

The table 1 reflects the decrease in the number of farms and the relative stable share of profitable farms with an average of 76.8% in the analyzed period. Financial indicators show the increase in average profitability and also the ability to generate profit without subsidies in last three years. One of the reason for the increase in 2014 is the ban on agricultural imports from EU and other countries. The structure of agricultural production in Orel region is presented in Table 2. Agricultural crop production in Orel region is focused on grain and sugar beet production. These two crops amount to 90% of the total agricultural production. Animal production is less than 10% and it is dominated by milk production. For our analysis we selected grain and milk production with the aim to compare risk and profit of crop and animal production in Russian Orel region.
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Assessment of Rural Women’s Participation in Agricultural Production

Assessment of Rural Women’s Participation in Agricultural Production

A major and often overlooked feature of LDCs agrarian systems, particularly in sub- Saharan Africa, is the crucial role played by women in agricultural production (Smith and Todaro, 2006). In Africa, where subsistence farming is predominant and shifting cultivation remains important, nearly all tasks associated with subsistence food production are performed by women. In some cases, they were found to do 70% and in other case they perform nearly 80% of the total. Typically, these tasks are performed only with the most primitive tools and hard labor simply to produce enough output to meet the family subsistence requirements, while the men often attempt to generate cash income through work on nearby plantations or in the cities (ibid). In sub-Saharan Africa, subsistence farming is usually and essentially a female activity. Women are the primary laborers on small farmers where they contribute 2/3 or more of all hours of work on food production, processing and marketing are female responsibilities (ibid).
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The Impact of Institutional Credit on Agricultural Production in Pakistan

The Impact of Institutional Credit on Agricultural Production in Pakistan

This study is based on the secondary data collected from various publications of government of Pakistan and office records of the ZTBL/ADBP. The data regarding variables of interest pertains to the financial years 1971-72 to 2001-2002. The study would compute various credit indicators, calculate shares of various financial institutions in total agricultural loans advanced, evaluate purpose wise composition of agricultural credit, and estimate the agricultural production function using agricultural credit as one of the explanatory variables. Conventionally, agricultural production function represents relationship between physical quantities of output and the inputs like land, labour, capital and quantities of other inputs (like water, fertiliser, pesticides etc.). However, as agriculture is a multi-product industry therefore, Agricultural Gross Domestic Product (AGDP) was used as the dependent variable and agricultural production is assumed to be the function of water availability, agricultural labour force, cropped area, and agricultural credit. Other important inputs like tractors, fertiliser, biocides, and improved seeds etc. that may be purchased by using credit money were dropped and agricultural credit was directly introduced as one of the explanatory variables.
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Strategy for SMEs in the area of primary agricultural production

Strategy for SMEs in the area of primary agricultural production

Abstract: Th e goal of the paper was to defi ne recommendations of a suitable business strategy for SMEs in the area of primary agricultural production. First, the external environment was analysed to defi ne suitable recommendations for the industry, and also to discover opportunities and threats through fi tting strategic analyses, that aff ect the aforementioned businesses. Secondly, it was necessary to get familiar with the internal environment and functioning of specifi c businesses in the industry (namely those that operate in the Western region of Prostějov at the foot of the Drahanska Highlands) and to evaluate their effi ciency to defi ne their strengths and weaknesses. Th e paper demonstrates that there is a wide range of means that can be taken advantage of when defi ning fi tting strategies for agricultural businesses, e.g. the Porter’s Five Forces Model, the IFE and the IE Matrix, the SWOT Matrix as well as the general strategies by Ansoff . Among the most important recommendations for increasing the competitiveness of SMEs in the agricultural industry, there belong: taking advantage of all the suitable agricultural subsidies, taking advantage of accredited agricultural advisors, making sure that the legal requirements of governmental bodies are met, increasing revenues for arable farming (through a good quality seed stock, chemicals and fertilizers, the foundation of suitable storage facilities in order to postpone the sales of the grain after the harvest season), creating sales cooperatives/associations to balance the competing force of buyers, rejuvenating the workforce, solidifying the ownership structure in the businesses, entrepreneurial diversifi cation (electrical energy produc- tion), increasing quality in animal farming.
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Management of agricultural production in the conditions of information society

Management of agricultural production in the conditions of information society

Abstract: Nowadays the development of information systems and technologies secures an effective solution of the produ- ction process. The implementation of a qualitative software solution is a competitive advantage for agricultural companies as well. In this article, we pay our attention to problematic fields which need to be respected in the process of automated management of agricultural production. We consider as the most important part the creation of adequate information in- frastructure which respects the company organization structure and manager’s requirements for information accessibility; data monitoring in terms of environmental economy (nitrate directive) and we suggest selected aspects for automated com- pany information system.
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Effects of Agricultural Insurance Scheme on Agricultural Production in Ondo State

Effects of Agricultural Insurance Scheme on Agricultural Production in Ondo State

This study was carried out to see how well the objectives of the Nigerian Agricultural Insurance Scheme had been achieved in Ondo State. Multi-stage sampling technique was used to select 120 insured farmers from two local government areas and a well-structured questionnaire was used to collect data from the farmers. The data collected were analyzed using descriptive statistics. The analysis shows that accessibility to credit was the only reason the farmers participated in the insurance scheme. However, the farmers affirmed that there was increase in investments which brought about increases in their output. Thus, their accessibility to farm credit could be said to be responsible for these increases. Hence, some of the objectives of the agricultural insurance scheme to increase agricultural production and accessibility to credit had been achieved.
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Expanding Agricultural Production in Tanzania

Expanding Agricultural Production in Tanzania

As agriculture accounts for a large share of employment, export earnings and even GDP in Tanzania, the sector is seen as a main vehicle in any national economic strategy to combat poverty and enhanced agricultural productivity is crucial to realize this objective. Despite this, there are no comprehensive studies of agricultural production and productivity using farm-level data in Tanzania. The National Panel Surveys (NPS) of 2008/09 and 2010/11 provide extensive data on some 3,280 farm households sampled throughout Tanzania, including information on area planted, quantity and value of harvest and input use (purchased and household) for a wide a variety of crops. Analysis of these panels offers the potential to provide insight on the determinants of productivity and supply response, in particular to identify factors amenable to policy influence that can provide effective incentives for farmers to increase production and efficiency. The basic aim of the scoping study is to assess the potential of analysing this data by describing and summarising the information.
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ECONOMIC-MATHEMATICAL MODELING OF OPTIMIZATION PRODUCTION OF AGRICULTURAL PRODUCTION

ECONOMIC-MATHEMATICAL MODELING OF OPTIMIZATION PRODUCTION OF AGRICULTURAL PRODUCTION

The study of the model on the set of other variants of the development of agricultural production appearing in the process of solving allows us to study and find the best option in accordance with the agreed optimization criterion. The selected criterion, according to the established task, meets the requirement to ensure the fulfillment of obligations for the supply of products in accordance with contracts and in public funds. Thus, the circle of interests and the customer of the product, and its producers are taken into account [3].

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Malaria and Agricultural Production in Nigeria

Malaria and Agricultural Production in Nigeria

Malaria has adverse effect on agricultural production in Nigeria. This study examined the effect of malaria morbidity on the agricultural Gross Domestic Product (GDP) in Nigeria as well as predicts the future incidence of malaria in Nigeria. Secondary data employed in the study were analyzed using descriptive statistics, production function and Markov chain analysis. Findings from the study revealed that the highest percentage change in malaria morbidity and mortality were recorded in the period between 1996 and 2000 just before the Millennium Development Goal declaration. Malaria impacted negatively on agriculture production in the country both in the short and long-run. For every one reported case of malaria per 100,000 persons in the country, while holding other explanatory variables constant, Agric. GDP will reduce by N 0.762 per year. Projections of malaria morbidity in Nigeria revealed that by the end of the year 2040, the number of people that will die of malaria for every reported case will increase. The paper recommends that efforts should be directed towards the complete extermination of malaria vectors.
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What is Behind the Fall in Russian Agricultural Production?

What is Behind the Fall in Russian Agricultural Production?

Crop producing farms, being mostly profitable, were expected to be less liquidity con- strained than those specialized in animal husbandry production. Therefore, one would expect that extra source of liquidity granted to livestock production will improve its level more efficiently than for crop producing farms. Estimation results for two sub-samples of enterprises (crop and animal husbandry) demonstrate that there is no difference in coefficients of financial variables and thus there is no major difference in performance of these types of farms in relation to financial environ- ment. Different signs of output elasticity, however, points to a weak evidence that livestock produc- ers are lacking buildings and constructions and other relevant equipment. Furthermore, it might in- dicate that crop producers accumulate overvalued stock of assets such as machinery, harvesting equipment, constructions that are no longer used in production but are still on the balance sheet. Labour and materials are less constrained inputs on livestock farms (smaller magnitude of elasticity coefficient) than on crop farms.
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The impact of fixed assets on Polish agricultural production

The impact of fixed assets on Polish agricultural production

duction in Polish agriculture from 2002–2005. But the overall productivity of fixed assets only explained 30–54% of the variation in these three categories of production. This was reflected in the greater dis- persion within the gross values of fixed assets than their productivity in agriculture for the period under investigation. The impact of productivity of gross fixed assets was 1.5 times higher than of the gross value of the total fixed assets. Therefore, produc- tivity of gross fixed assets limited considerably the decline in work performance in the Polish farming in the years 2002–2005. It is also an intensive driver of work performance growth in farming.
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