Farm Credit East's AgEnhancement Grants

2019 ◽  
Vol 34 (10) ◽  
pp. 13-13
Keyword(s):  
Farm Credit

The demand for US federal farm credit legislation

Agribusiness ◽  
1988 ◽  
Vol 4 (4) ◽  
pp. 371-383 ◽  
Author(s):  
Utpal Vasavada ◽  
Terence J. Centner
Keyword(s):  
Farm Credit

scholarly journals Investigation of Factors Influencing the Technical Efficiency of Agricultural Producers Participating in Farm Credit Programs: The Case of Greece

2003 ◽  
Vol 35 (3) ◽  
pp. 529-541 ◽  
Author(s):  
Anthony N. Rezitis ◽  
Kostas Tsiboukas ◽  
Stauros Tsoukalas
Keyword(s):  
Technical Efficiency ◽  
Family Income ◽  
Stochastic Frontier ◽  
Agricultural Producers ◽  
Credit Program ◽  
Factors Influencing ◽  
Efficiency Measures ◽  
Farm Credit ◽  
Negative Effect ◽  
Hired Labor

This study investigates a number of factors influencing technical efficiency of Greek farms participating in the 1994 European Union (EU) farm credit program. Technical efficiency measures are obtained within the framework of a parametric stochastic frontier. Factors showing a positive effect on technical efficiency are value of liabilities, number of hours of mechanical operation, large land size, and rental land, whereas those showing a negative effect are value of EU product subsidies, value of off-farm family income, and hired labor. The value of investments incurred by farms because of their participation in the 1994 farm credit program does not show any significant effect on technical efficiency. The predicted levels of technical efficiency indicate that the average technical efficiency of farms 3 years after participating in the 1994 farm credit program is lower than the average technical efficiency of the same farms the year before participating in the program. Thus, the program has failed to increase the efficiency of farms.

scholarly journals Is change worth it? The effects of adopting modern agricultural inputs on household welfare in Rwanda

2021 ◽  
Vol 16 (3) ◽  
pp. 216-236
Author(s):  
Aimable Nsabimana ◽  
Keyword(s):  
Market Access ◽  
Driving Forces ◽  
Farm Income ◽  
Household Welfare ◽  
Switching Regression ◽  
Adoption Decision ◽  
Advisory Services ◽  
Effective Dissemination ◽  
Farm Credit ◽  
Household Farm

This study investigates the driving factors that influence farmers’ decisions to adopt modern agricultural inputs (MAI) and how this affects farm household welfare in rural Rwanda. To account for heterogeneity in the MAI adoption decision and unobservable farm and household attributes, we estimate an endogenous switching regression (ESR) model. The findings reveal that size of land endowment, access to farm credit and awareness of farm advisory services are the main driving forces behind MAI adoption. The analysis further shows that MAI adoption increases household farm income, farm yield and equivalised consumption per capita. This implies that adopting MAI is the most consistent and potentially best pathway to reduce poverty among rural farmers. The study hence suggests that policymakers should align the effective dissemination of MAI information and farm advisory services, strengthen farm credit systems and improve market access – most crucially at affordable prices – among small-farmers throughout Rwanda.

Credit as a Tool of Statecraft

2019 ◽  
pp. 69-87
Author(s):  
Sarah L. Quinn
Keyword(s):  
United States ◽  
Central Government ◽  
National Level ◽  
The United States ◽  
State Building ◽  
Public Private Partnerships ◽  
Credit Allocation ◽  
Self Help ◽  
Farm Credit ◽  
Policy Arenas

This chapter shows how Progressives returned to the issue of farm credit distribution in the early 1900s and drew on European precedents to reframe credit allocation as a way for the central government to help people help themselves. American Progressives thus replaced their earlier, more radical farm credit politics with a more moderate vision of government-supported credit as an inexpensive way of supporting self-help. The chapter then considers the Federal Farm Loan Act (FFLA). Compared with other hallmarks of Progressive Era state building, the FFLA seems relatively unimportant. Nevertheless, it was a turning point in the use of selective credit as a tool of federal statecraft in the United States. The FFLA provided federal credit on a national level that was administered through public–private partnerships and bolstered by tax expenditures. By tracing the lead-up to this policy, one can see how Progressives forged a new array of cultural and organizational approaches to federal credit that would later proliferate across policy arenas.

Markov chain models for farm credit risk migration analysis

2007 ◽  
Vol 67 (1) ◽  
pp. 99-117 ◽  
Author(s):  
Xiaohui Deng ◽  
Cesar L. Escalante ◽  
Peter J. Barry ◽  
Yingzhuo Yu
Keyword(s):  
Markov Chain ◽  
Credit Risk ◽  
Markov Chain Models ◽  
Farm Credit ◽  
Migration Analysis

scholarly journals Selecting The “Best” Prediction Model: An Application To Agricultural Cooperatives

1992 ◽  
Vol 24 (1) ◽  
pp. 163-169 ◽  
Author(s):  
Alicia N. Rambaldi ◽  
Hector O. Zapata ◽  
Ralph D. Christy
Keyword(s):  
Credit Scoring ◽  
Scoring Function ◽  
Poor Performance ◽  
Agricultural Cooperatives ◽  
Agricultural Cooperative ◽  
Out Of Sample ◽  
Farm Credit ◽  
Rank Transformation ◽  
Out Of Sample Prediction ◽  
Credit Worthiness

AbstractA credit scoring function incorporating statistical selection criteria was proposed to evaluate the credit worthiness of agricultural cooperative loans in the Fifth Farm Credit District. In-sample (1981-1986) and out-of-sample (1988) prediction performance of the selected models were evaluated using rank transformation discriminant analysis, logit, and probit. Results indicate superior out-of-sample performance for the management oriented approach relative to classification of unacceptable loans, and poor performance of the rank transformation in out-of-sample prediction.

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