Modelling and interpreting the impact of policy and price scenarios on farm-household sustainability: Farming systems vs. result-driven clustering

2013 ◽  
Vol 43 ◽  
pp. 96-108 ◽  
Author(s):  
D. Viaggi ◽  
M. Raggi ◽  
S. Gomez y Paloma
Keyword(s):  
Farming Systems ◽  
Farm Household ◽  
On Farm ◽  
The Impact

The Impact of Rural Taxation Reform on Farm Household Income

2009 ◽  
Vol 42 (4) ◽  
pp. 75-90 ◽  
Author(s):  
Cuiping Xu ◽  
Qinghua Shi ◽  
H. Holly Wang
Keyword(s):  
Household Income ◽  
Farm Household ◽  
Taxation Reform ◽  
On Farm ◽  
The Impact

The Impact of Regular Research Field Hearings (RRFH) in On-farm Trials in Northeast Brazil

1988 ◽  
Vol 24 (3) ◽  
pp. 281-288 ◽  
Author(s):  
Greg Baker ◽  
Hendrik C. Knipscheer ◽  
Jose De Souza Neto
Keyword(s):  
Agricultural Research ◽  
Farming Systems ◽  
Research Field ◽  
Performance Criteria ◽  
Northeast Brazil ◽  
Test Results ◽  
Systems Research ◽  
On Farm ◽  
The Impact ◽  
Farming Systems Research

SUMMARYThe study of livestock in farming systems research presents several challenges. Factors such as the small number of observations, high statistical variability in measurements, and the difficulty in valuing non-market inputs and outputs make it difficult to evaluate on-farm research. Regular Research Field Hearings (RRFH) can improve communication between scientists and farmers, thereby increasing mutual understanding, willingness to collaborate in evaluation, potential technologies, and also improving test results. Of three groups of sheep and goat farmers involved in on-farm testing in northeast Brazil, the group which participated in RRFH experienced a significantly higher level of success with the technology as measured according to performance criteria such as weight gains and farmers' perceptions of their animals' performance. The results confirm the importance of farmers' participation in agricultural research.

Multivariate classification of farming systems for use in integrated pest management studies

1999 ◽  
Vol 79 (4) ◽  
pp. 647-654 ◽  
Author(s):  
J. Y. Leeson ◽  
J. W. Sheard ◽  
A. G. Thomas
Keyword(s):  
Management Practices ◽  
Farming Systems ◽  
Minimum Variance ◽  
Farm Management ◽  
Management Systems ◽  
Multivariate Techniques ◽  
Organic Systems ◽  
On Farm ◽  
The Impact

On-farm studies provide a realistic setting to examine the impact of interactions of management practices on weed communities within various farming systems. Clustering farm units into groups that use similar management practices enables the replication of farm management systems within on-farm studies. The goal of this study is to objectively classify farm units into management systems on the basis of quantitative variables describing aspects of cropping history and chemical input levels using multivariate techniques. Twenty-eight Saskatchewan farmers provided details of their management practices from 1990 to 1997 through a series of questionnaires. Twelve variables derived from the questionnaires were used to describe cropping history, pesticide, tillage and fertilizer use on each farm unit. These variables were used to cluster farm units using minimum variance classification and NMS ordination. Both techniques identified seven farm management systems. The greatest differences were observed between organic and non-organic systems. Farm management systems that used annual fallow and continuous annual cropping histories were most similar. The consistent results obtained by use of the two unrelated methodologies indicate the utility of this approach for the classification of farm management systems. Key words: Farm management system, classification, cropping history, chemical input level, on-farm

scholarly journals Technology Adoption and Off-Farm Household Income: The Case of Herbicide-Tolerant Soybeans

2005 ◽  
Vol 37 (3) ◽  
pp. 549-563 ◽  
Author(s):  
Jorge Fernandez-Cornejo ◽  
Chad Hendricks ◽  
Ashok Mishra
Keyword(s):  
Technology Adoption ◽  
Household Income ◽  
Nationwide Survey ◽  
Farm Household ◽  
Agricultural Technologies ◽  
Farm Work ◽  
Total Household Income ◽  
Herbicide Tolerant ◽  
On Farm ◽  
The Impact

We model the interaction of off-farm work and adoption of agricultural technologies and the impact of adopting these technologies on farm household income from on farm and off-farm sources after controlling for such interaction, and estimate the model for the case of adoption of herbicide-tolerant (HT) soybeans using a nationwide survey of soybean farms for 2000. We find that adoption of HT soybeans is positively and significantly related to off-farm household income for U.S. soybean farmers, after controlling for other factors. In addition, while on-farm household income is not significantly related to adoption, total household income increases significantly with adoption.

scholarly journals Farm household income inequality in Slovenia

2020 ◽  
Vol 17 (4) ◽  
pp. e0112 ◽  
Author(s):  
Štefan Bojnec ◽  
Imre Fertő
Keyword(s):  
Income Inequality ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Household Income ◽  
Farm Income ◽  
Farm Household ◽  
Data Set ◽  
Market Income ◽  
On Farm ◽  
The Impact

Aim of study: To investigate the structure and evolution of farm household income and examine the contribution of different sources of farm household income, particularly the impact of Common Agricultural Policy reform on farm household income inequality in Slovenia.Area of study: Slovenia, one of the European Union member states.Material and methods: A panel data set was compiled using Slovenian Farm Accountancy Data Network data at farm level for the period 2007-2013. Total farm household income was disaggregated into two different components: 1) income components, which can contain market income and off-farm income, and 2) subsidy components, which can contain subsidies from Pillars 1 and 2. Pillar 2 support included subsidies related to agri-environmental measures, less favoured areas and other rural development measures. The income distribution and decomposition were examined using the Gini decomposition method to determine the contribution of each income source and the policy shift from market to government support on farm household income and overall inequality.Main results: A shift in Common Agricultural Policy and related measures determined the structure and evolution of farm household incomes. Off-farm income had a lesser and rather stable impact on farm household income inequality, while the major change involved an increase in the importance of subsidies from Pillar 2 which is consistent with a policy of targeting farms in less favoured areas. Subsidies from Pillar 1 reduced, while market income increased farm household income inequality.Research highlights: Subsidies in farm incomes increased. They could reduce farm household income inequality.

scholarly journals Assessing the Impact of Climate Resilient Technologies in Minimizing Drought Impacts on Farm Incomes in Drylands

2021 ◽  
Vol 14 (1) ◽  
pp. 382
Author(s):  
Josily Samuel ◽  
Chitiprolu Anantha Rama Rao ◽  
Bellapukonda Murali Krishna Raju ◽  
Anugu Amarender Reddy ◽  
Pushpanjali ◽  
...  
Keyword(s):  
Household Income ◽  
Farm Size ◽  
Farm Income ◽  
Farm Household ◽  
Cropping Pattern ◽  
Factors Influencing ◽  
Control Village ◽  
On Farm ◽  
The Impact ◽  
Livestock Rearing

Abstract: Asia is the region most vulnerable to climate change and India is ranked as one of the most climate vulnerable countries in the world, frequently affected by natural disasters. In this study, we investigated the impact of drought on crop productivity, farmer’s employment and income. The difference-in-difference model (DID) and stepwise multiple linear regression (MLR) were employed to quantify the impact of adopting climate resilient technologies (CRTs) on farm household income during a drought. The factors influencing farm incomes were analyzed using MLR. The study used survey data collected from the drought prone district of Telangana, India. Sixty farmers each from a village adopted under the National Innovations in Climate Resilient Agriculture (NICRA) program and a control village were interviewed. Primary data on the socio-economic characteristic of farmers, cropping pattern, income composition, productivity of major crops, employment and climate resilient interventions adopted by farmers were collected using a well-structured schedule. The results reveal that income crop cultivation was the major contributor to household income (60%) followed by livestock rearing. Farmers reported that droughts decreased the income from crops by 54 per cent and income from livestock rearing by 40 per cent. The farmers belonging to the climate resilient village had 35 per cent higher incomes compared to those in the control village and it was estimated to be Rs. 31,877/farm household/year during droughts using the DID estimate. Farm size, livestock possession, adoption of CRTs and investment in agriculture were the determining factors influencing farm income. Thus, farmers especially in drought prone regions need to be encouraged and supported to adopt cost effective, location specific climate resilient technologies.

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