scholarly journals Labour-use pattern on Swiss dairy farms

2013 ◽  
Vol 59 (No. 4) ◽  
pp. 149-159
Author(s):  
M. Lips ◽  
D. Schmid ◽  
P. Jan
Keyword(s):  
Spatial Data ◽  
Dairy Farms ◽  
Farm Size ◽  
Farm Income ◽  
Marginal Effect ◽  
Farm Work ◽  
Farm Family ◽  
Wage Labour ◽  
Use Pattern ◽  
On Farm

Abstract: Analysing the labour-use pattern on Swiss dairy farms, we apply a typology scheme with two criteria: on-farm wage labour and off-farm family labour. The resultant four farm types are analysed based on the data from the Swiss Farm Accountancy Data Network (FADN) as well as the spatial data on available jobs. Only 17% of dairy farms have neither on-farm wage labour nor off-farm family labour. 60 % have family members involved in off-farm activities. On average, 0.3 annual work units (AWU) are employed in off-farm activities, earning double the on-farm income per AWU. In line with the literature, we found that the likelihood of on-farm wage labour increases with the farm size and the degree of diversification. Involvement in off-farm activities is more likely if the farm manager is young and has a spouse with a non-agricultural education. Furthermore, private consumption per consumer unit has a positive marginal effect on the likelihood of off-farm work. Finally, no evidence was found of available jobs within a range of 10 kilometres acting as a proxy for the local labour demand for off-farm activities, leading us to the conclusion that involvement in off-farm work is an option for most of the analysed dairy farms.  

scholarly journals Does Off-Farm Work Improve Farm Income? Empirical Evidence from Tolon District in Northern Ghana

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Benjamin Tetteh Anang ◽  
Kwame Nkrumah-Ennin ◽  
Joshua Anamsigiya Nyaaba
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Empirical Evidence ◽  
Farm Size ◽  
Northern Ghana ◽  
Farm Income ◽  
Income Diversification ◽  
Farm Work ◽  
On Farm ◽  
The Impact

Participation of farm households in off-farm work has gained prominence in recent times as an income diversification strategy. The effect of off-farm work on farm income is however unclear. This paper therefore sought to provide empirical evidence of the income effect of off-farm activity participation using a cross section of maize farmers in Tolon District of Ghana as a case study. In order to account for sample selection bias, the Heckman selection model was used to estimate the factors influencing participation in off-farm work and the determinants of farm income. Furthermore, the study employed propensity score matching to evaluate the impact of off-farm work on farm income. The results indicate that participation in off-farm work is influenced by sex, age, and years of formal education of the respondent, farm size, and number of dependents while farm income is influenced by age of the respondent, farm size, and access to credit. In addition, the result of the propensity score matching revealed that participants in off-farm work increased their farm income by at least GH¢ 1702 as a result of income diversification. The rural economy therefore provides off-farm and on-farm linkages that enhance farmers’ income from agriculture. The creation of employment opportunities outside the farm will therefore complement on-farm work and enhance income from farming.

scholarly journals Effects of Multiple Risks on Farm Income and Willingness to Pay for Agricultural Insurance: A Case Study of the Greater Accra Region in Ghana

2016 ◽  
Vol 8 (6) ◽  
pp. 1
Author(s):  
Frederick Murdoch Quaye
Keyword(s):  
Risk Factors ◽  
Willingness To Pay ◽  
Significant Risk ◽  
Farm Size ◽  
Farm Income ◽  
Agricultural Insurance ◽  
Study Region ◽  
Multiple Risks ◽  
On Farm ◽  
Greater Accra Region

<p>This paper analyzes the determinants of farm income among farmers producing crops and animals in the Greater Accra Region of Ghana. It further estimates the willingness to pay for agricultural insurance by farmers. The farm income function was evaluated using a logarithmic function in which farm income is regressed as a function of determinants affecting it. The econometric results suggest that gender, education, farm size, farming experience, fertilizer usage and input cost all have a positive and statistically significant association with farm income. The results indicate that when investing in agriculture in the study region, weather hazards and pest and disease attacks are two important risk factors that need to be considered in the implementation of insurance policies since they have and statistically significant negative associations with farm income. The paper further observes that weather and pest/disease attacks are two significant risk factors that tend to influence farmers’ willingness to adopt and pay for agricultural insurance.</p>

scholarly journals Factors influencing the adoption of improved vegetable production practices among women farmers in Aguata-Agricultural Zone, Anambra State, Nigeria

Agrosearch ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 67-81
Author(s):  
L.E. Odoemlam ◽  
F.C. Nzeakor
Keyword(s):  
New Technologies ◽  
Women Farmers ◽  
Farm Size ◽  
Farm Income ◽  
Vegetable Production ◽  
Probit Regression ◽  
Factors Influencing ◽  
Production Practices ◽  
Adoption Level ◽  
On Farm

The study examined the level and determinants of adoption of improved vegetable production practices in the study area. A three-stage sampling procedure was used in the selection of 160 respondents. A structured questionnaire was used for data collection. Data collected were analysed using descriptive statistics and Probit regression model. Results on adoption level of the selected improved vegetable production practices indicated that improved seeds had a grand mean of 𝑥̅ = 3.17, field preparation (𝑥̅ = 3.19), planting distance (𝑥̅ = 2.99), water management (𝑥̅ = 3.43), fertilizer/organic manure application (𝑥̅ = 3.55), pesticides (𝑥̅ = 2.57), harvesting ( 𝑥̅= 5.00) and storage procedure (𝑥̅ = 4.89) based on 5-point Likert scale adoption level. On factors influencing adoption of improved vegetable production practices, the result revealed that farm size (1.00188***), credit access (4.704902**), on-farm demonstration (2.900749**) and farm labour (1.295902***) had a positive and significant influence on improved vegetable production practices by the respondents. The result further indicated that the age (0.3135258***) and the off-farm income (0.0870768) of the farmers had a negative influence on the adoption of improved vegetable production practices. Based on these findings, the study revealed that the women farmers could have full adoption of the improved production practices if the factors are adequately addressed. The study therefore recommends that before the introduction of a new technology, the ADPs should ensure that maximum audience analysis is carried out to address some of the factors influencing adoption. Besides, introduction of new technologies to farmers should go hand-in-hand with on-farm demonstration since it is by that they would develop confidence and allay their fears associated with improved practices.

scholarly journals Profitability on dairy farms with automatic milking systems compared to farms with conventional milking systems

2019 ◽  
Vol 22 (2) ◽  
pp. 215-228 ◽  
Author(s):  
Bjørn Gunnar Hansen ◽  
Hans Olav Herje ◽  
Jonas Höva
Keyword(s):  
Dairy Farms ◽  
Ordinary Least Squares ◽  
Transitional Period ◽  
Farm Size ◽  
Farm Income ◽  
Least Squares Regression ◽  
Farm Production ◽  
Automatic Milking ◽  
The Difference ◽  
Automatic Milking Systems

The objective of this study was to explore differences in profitability between farms with automatic milking systems (AMS) and farms with conventional milking systems (CMS). To explore profitability, we analysed the gross farm income from dairy cows. Accounting and production data for over a thousand dairy farms were collected. Using kernel-matching, we made CMS farms more comparable to AMS farms. We then used ordinary least squares regression to estimate the effect of AMS relative to farm size and time passed since last investment in milking systems. The results show that farms must have 35 to 40 cows before AMS becomes more profitable than CMS. Further, any profitability gains will only be visible after a transitional period of approximately four years. Milk revenues are higher on AMS farms, and the difference increases with the size of the farm. Production-related costs are also higher on AMS farms.

scholarly journals A Global Review of Monitoring, Modeling, and Analyses of Water Demand in Dairy Farming

2020 ◽  
Vol 12 (17) ◽  
pp. 7201
Author(s):  
Philip Shine ◽  
Michael D. Murphy ◽  
John Upton
Keyword(s):  
Water Use ◽  
Water Intake ◽  
Prediction Accuracy ◽  
Dry Matter ◽  
Free Water ◽  
Dairy Farms ◽  
Machine Learning Algorithms ◽  
Dry Matter Intake ◽  
Farm Size ◽  
On Farm

The production of milk must be balanced with the sustainable consumption of water resources to ensure the future sustainability of the global dairy industry. Thus, this review article aimed to collate and summarize the literature in the dairy water-usage domain. While green water use (e.g., rainfall) was found to be largest category of water use on both stall and pasture-based dairy farms, on-farm blue water (i.e., freshwater) may be much more susceptible to local water shortages due to the nature of its localized supply through rivers, lakes, or groundwater aquifers. Research related to freshwater use on dairy farms has focused on monitoring, modeling, and analyzing the parlor water use and free water intake of dairy cows. Parlor water use depends upon factors related to milk precooling, farm size, milking systems, farming systems, and washing practices. Dry matter intake is a prominent variable in explaining free water intake variability; however, due to the unavailability of accurate data, some studies have reported moving away from dry matter intake at the expense of prediction accuracy. Machine-learning algorithms have been shown to improve dairy water-prediction accuracy by 23%, which may allow for coarse model inputs without reducing accuracy. Accurate models of on-farm water use allow for an increased number of dairy farms to be used in water footprinting studies, as the need for physical metering equipment is mitigated.

scholarly journals EFFECTS OF AGRICULTURAL SUBSIDIES ON INCOME RISK IN LITHUANIAN DAIRY FARMS

2016 ◽  
Vol 38 (4) ◽  
pp. 351-358 ◽  
Author(s):  
Imre Ferto ◽  
Aldona Stalgienė
Keyword(s):  
Panel Data ◽  
Positive Impact ◽  
Dairy Farms ◽  
Econometric Models ◽  
Farm Size ◽  
Farm Income ◽  
Income Risk ◽  
Agricultural Subsidies ◽  
Coefficients Of Variation ◽  
Income Variability

The aim of the paper is to investigate the effects of agricultural subsidies on income variability of Lithuanian dairy farms. In addition, the observed heterogeneity in income risks across farms and time is explained in terms of farm characteristics. It was employed balanced farm-level panel data of the Lithuanian farm accountancy network (FADN) was used to construct coefficients of variation of five-year gross farm revenues over the period 2010 to 2014. Various econometric models are applied to measure the effect of off-farm income, total subsidies, farm size, and financial immobility on the variability of gross farm incomes. Estimations suggest that agricultural subsidies, liquidity have positive impact on income risk. The age of farmers negatively influences the income risk. There is non-linear relationship between farm size and income risk.

scholarly journals Factors Affecting Farm and Non-Farm Income of Haor Inhabitants of Bangladesh

2013 ◽  
Vol 23 (1-2) ◽  
pp. 143-150 ◽  
Author(s):  
MT Parvin ◽  
M Akteruzzaman
Keyword(s):  
Family Size ◽  
Farm Size ◽  
Farm Income ◽  
Factors Affecting ◽  
Second Development ◽  
Negative Effect ◽  
Nonfarm Income ◽  
Log Linear ◽  
On Farm ◽  
Positive Effect

The study has been conducted to examine the factors influencing farm and nonfarm income of Haor economy in Bangladesh. Dingaputa Haor area of Netrokona district was selected for the present study and a sample of 60 farmers had been taken randomly. The log linear form of Cobb-Douglas production function was chosen to determine the effects of socioeconomic variables on farm income and non-farm income. Apart from this, some descriptive statistical analysis were done to examine the socioeconomic characteristics of sampled households. The estimated results of the regression models revealed that family size and farm size had a significant positive effect on farm income and non-farm income had a significant negative effect on farm income. On the other hand, family size had a positive and significant effect on non-farm income and farm income had a negative and significant effect on non-farm income. To promote the farm and non-farm sector income and strengthening its potential linkages between them, the study mainly recommends increasing efforts on two fronts: first, reforming the institutions responsible for rural development and second, development activities and projects that would enhance farm and non-farm income and the linkages between them.DOI: http://dx.doi.org/10.3329/pa.v23i1-2.16578Progress. Agric. 23(1 & 2): 143 – 150, 2012

scholarly journals Farmland Rental Participation, Agricultural Productivity, and Household Income: Evidence from Rural China

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 899
Author(s):  
Wenjing Han ◽  
Zhengfeng Zhang ◽  
Xiaoling Zhang ◽  
Li He
Keyword(s):  
Rural Households ◽  
Agricultural Productivity ◽  
Household Survey ◽  
Farm Size ◽  
Farm Income ◽  
Rural Land ◽  
Total Income ◽  
Land System ◽  
Rural Land System ◽  
On Farm

The rural land rental market is playing an increasingly important role in the agricultural transformation period for developing countries, including China, where rural farmland rental is highly context-specific with the implementation of the collective-owned rural land system; thus, in turn, the access to farmland rental markets for rural households has profoundly influenced their livelihood strategies and income earnings. This paper investigates the income impact differences caused by rural households’ farmland rental participation activities and explores such impact mechanisms by further evaluating the income impacts caused by rental area and household agricultural productivity. Data from the Chinese national household survey were used for estimating the empirical models. Our results show that farmland renting has positively affected households’ on-farm and total income, but there is no significant effect upon off-farm income. According to income differences across quantiles, we find households with high on-farm income are more sensitive about enlarging their farm size by renting farmland, and households with middle and upper-middle off-income may benefit more from renting out their farmland. Furthermore, the joint effects of renting area and household agricultural productivity on lessee households’ farm income is significantly positive. For lessor households, our results indicate that renting out farmland did not improve their off-farm and total income as it may have a limited effect on farm household labor distribution. Our findings suggest that engaging in farmland rental activity can enhance farming productivity efficiency and poverty alleviation among rural households. Under the collective-owned rural land system, it is urgent and necessary to initiate and design incentive policies to encourage highly efficient large farms to expand the farm size and provide smallholders with equal opportunities to engage in farmland rental activities.

scholarly journals Wpływ subsydiów wspólnej polityki rolnej na dochody gospodarstw mlecznych FADN w krajach Unii Europejskiej w latach 2004-2013

2017 ◽  
Vol 17(32) (1) ◽  
pp. 53-62
Author(s):  
Marta Guth ◽  
Katarzyna Smędzik-Ambroży
Keyword(s):  
Agricultural Policy ◽  
Dairy Farm ◽  
Dairy Farms ◽  
Farm Income ◽  
Family Farm ◽  
Panel Regression ◽  
Decoupled Payments ◽  
The Common ◽  
On Farm ◽  
The Impact

The main aim of the article was to assess the impact of the Common Agricultural Policy subsidies on the income of FADN dairy farms in 2004-2013. The share of total subsidies, including subsidies on investments on farm income per unit of work (FWU) on FADN dairy farms in EU countries, with the division to EU-12 and EU-15, was examined. The trend of family farm income with and without subsidies during the period under review was presented. In order to demonstrate which of the groups of subsidies had the greatest impact on family farm income, a panel regression was conducted. It turned out that the most significant for the FADN dairy farm income in 2005-2013 was decoupled payments and additional aid with other support.

scholarly journals Economic Resilience of EU Dairy Farms: An Evaluation of Economic Viability

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 510
Author(s):  
Artur Wilczyński ◽  
Ewa Kołoszycz
Keyword(s):  
Dairy Farms ◽  
Research Area ◽  
Economic Viability ◽  
Farm Income ◽  
The European Union ◽  
Economic Resilience ◽  
Production Scale ◽  
Unpaid Labor ◽  
On Farm ◽  
Economic Size

The economic viability of dairy farms is a measure of their ability to survive and grow. Its measurement is particularly important in periods of changes taking place in the environment of these entities. The last decade of the European dairy market was characterized by significant changes in regulations, which resulted in fluctuations in farm gate milk prices and, consequently, impacts on farm income. The main objective of the research was to assess the economic viability of dairy farms located in the European Union. The research area covered the countries that have the most raw cows’ milk delivered to dairies in the EU, and FADN data from 2009 to 2018. A comparative analysis was carried out on the level of temporal viability and permanent viability of farms classified by economic size. The research results showed that better temporal viability was achieved by farms with a larger production scale. On the other hand, the permanent economic viability was lower on farms belonging to a higher economic size class. Most of the analyzed groups of farms were in the survival phase. This means that dairy farms struggled to meet the costs of unpaid labor. Including direct payments in the calculation resulted in an improvement in temporal viability only in farms with the lowest economic size classes.

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