An Exploratory Investigation into the Farm Credit Market in New Zealand

<p>An increasing interest in the finance of farming in New Zealand has emerged in recent years. This is a result of three major developments: (i) the increasing reliance of the farm sector on external sources of finance. For instance, debt per farm has been increasing at an annual compound rate of 12% between 1963 and 1970; (ii) the effect of recent economic phenomena, such as falling product prices and a high rate of internal inflation, which have highlighted the question of a farm debt "burden"; (iii) the increasing sophistication of the New Zealand economy. This has offered the community alternative investment opportunities and has raised the question of availability of finance for farmers to sustain and increase their production.</p>

An Exploratory Investigation into the Farm Credit Market in New Zealand

<p>An increasing interest in the finance of farming in New Zealand has emerged in recent years. This is a result of three major developments: (i) the increasing reliance of the farm sector on external sources of finance. For instance, debt per farm has been increasing at an annual compound rate of 12% between 1963 and 1970; (ii) the effect of recent economic phenomena, such as falling product prices and a high rate of internal inflation, which have highlighted the question of a farm debt "burden"; (iii) the increasing sophistication of the New Zealand economy. This has offered the community alternative investment opportunities and has raised the question of availability of finance for farmers to sustain and increase their production.</p>

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

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.

What Determines the Uptake of Multiple Tools to Mitigate Agricultural Risks among Hybrid Maize Growers in Pakistan? Findings from Field-Level Data

Arable farming is an intrinsically risky enterprise. Therefore, managing risks and uncertainties in agriculture is very important as it affects all sectors of the economy of a developing country like Pakistan. To do so, a whole suite of options is available to the farming community to safeguard against any type of risk. However, farmers’ behavior of the concurrent adoption of multiple risk management tools is largely ignored in previous studies and has formed the rationale for this research. Thus, the current study is intended to investigate farmers’ decisions of adopting risk management strategies (contract farming, off-farm income diversification, and farm credit use) and to examine the impacts of a variety of factors on farmers’ risk management decisions. The present study is carried out in four districts of Punjab province, Pakistan with a focus on hybrid maize growers. A multivariate probit model is used to evaluate the impacts of independent variables on growers’ choices of adopting contract farming, off-farm income diversification, and farm credit use to manage farm risks keeping in view the potential for the concurrent adoption of these risk management strategies. Results show that 78% of farmers are risk-averse and hence ready to manage risks. The top risk faced by farmers is price risk followed by biological, climatic, and financial risks. Contract farming is the most popular strategy (61% farmers) followed by off-farm income diversification (49% farmers), and the use of farm credit (42% farmers). The findings also reveal that the decisions of adopting risk management strategies are interlinked while the adoption of one risk management tool complements farmers’ decision to adopt other risk management strategies. In addition, the risk management strategies’ adoption choices are affected by the number of factors including socioeconomic characteristics, farmers’ risk perceptions about risk sources, and their attitude towards risk. The study recommends the provision of timely information (climatic, extension) along with easy access to farm credit and the streamlining of contractual arrangements.

Household’s Agricultural Vulnerability to Climate Induced Disasters: A Case on South-west Coastal Bangladesh

Climatic events have a significant impact on south-western coastal agriculture in Bangladesh. The purpose of this study was to assess household’s agricultural vulnerability to climate-induced disasters and to identify the sub-indicators of adaptive capacity that determine the agricultural vulnerability to climate-induced disasters of south-western coastal households in Bangladesh. The vulnerability has been calculated by taking the Intergovernmental Panel on Climate Change (IPCC) concept through an Agricultural Vulnerability Index (AVI). Then, the ordered logit model has been employed to identify the key sub-indicators of adaptive capacity that determine the agricultural vulnerability to climate-induced disasters. A survey of 346 household heads from the two villages (181 household’s head from Sutarkhali and 165 household’s head from Nalian) of Sutarkhali Union of Dacope Upazila under Khulna District has been used in this study. Findings reveal that the mean score of Sutarkhali is 0.703 (high) for exposure, 0.762 (high) for sensitivity, 0.397 (low) for adaptive capacity and finally, the AVI is 0.689 (high). In the same fashion, Nalian has an average score of 0.658 (high) for exposure, 0.681 (high) for sensitivity, 0.410 (low) for adaptive capacity, and finally 0.643 (high) for the AVI. Non-farm employment, ownership of livestock, access to irrigation pump, improved crop diversification/ saline tolerant High Yielding Variety (HYV) crops, and access to farm credit have been found statistically significant sub-indicators of adaptive capacity that determine the agricultural vulnerability of both study sites. Finally, it is recommended that the intervention required for coastal adaptation of agriculture should be initiated by respective authorities.

Household’s Agricultural Vulnerability to Climate Induced Disasters: A Case on South-west Coastal Bangladesh

Climatic events have a significant impact on south-western coastal agriculture in Bangladesh. The purpose of this study was to assess household’s agricultural vulnerability to climate-induced disasters and to identify the sub-indicators of adaptive capacity that determine the agricultural vulnerability to climate-induced disasters of south-western coastal households in Bangladesh. The vulnerability has been calculated by taking the Intergovernmental Panel on Climate Change (IPCC) concept through an Agricultural Vulnerability Index (AVI). Then, the ordered logit model has been employed to identify the key sub-indicators of adaptive capacity that determine the agricultural vulnerability to climate-induced disasters. A survey of 346 household heads from the two villages (181 household’s head from Sutarkhali and 165 household’s head from Nalian) of Sutarkhali Union of Dacope Upazila under Khulna District has been used in this study. Findings reveal that the mean score of Sutarkhali is 0.703 (high) for exposure, 0.762 (high) for sensitivity, 0.397 (low) for adaptive capacity and finally, the AVI is 0.689 (high). In the same fashion, Nalian has an average score of 0.658 (high) for exposure, 0.681 (high) for sensitivity, 0.410 (low) for adaptive capacity, and finally 0.643 (high) for the AVI. Non-farm employment, ownership of livestock, access to irrigation pump, improved crop diversification/ saline tolerant High Yielding Variety (HYV) crops, and access to farm credit have been found statistically significant sub-indicators of adaptive capacity that determine the agricultural vulnerability of both study sites. Finally, it is recommended that the intervention required for coastal adaptation of agriculture should be initiated by respective authorities.

Household’s Agricultural Vulnerability to Climate Induced Disasters: A Case on South-west Coastal Bangladesh

The purpose of this study was to assess household’s agricultural vulnerability to climate induced disasters and to identify the indicators of adaptive capacity that determine the vulnerability of south-western coastal household’s in Bangladesh. The vulnerability was calculated by taking the Intergovernmental Panel on Climate Change (IPCC) concept through an Agricultural Vulnerability Index (AVI). Then the ordered logit model was employed in order to identify key determinants of agricultural vulnerability to climate induced disasters. A survey of 346 household’s head from the two settlements (181 household’s head from Sutarkhali and 165 household’s head from Nalian) of Sutarkhali Union of Dacope Upazila under Khulna District was used in this study. Findings revealed that the mean score of Sutarkhali was 0.703 (high) in exposure, 0.724 (high) in sensitivity, 0.341 (low) in adaptive capacity and finally, the agricultural vulnerability index (AVI) was 0.695 (high). On the same fashion, Nalian was an average score of 0.697 (high) in exposure, 0.721 (high) in sensitivity, 0.386 (low) in adaptive capacity, and finally 0.677 (high) in agricultural vulnerability index (AVI). Annual savings, formal education, ownership of livestock, improved seeds supply, access to irrigation pump, improved crop diversification/ High Yielding Variety (HYV) crops, access to large farm size and access to farm credit were found to be statistically significant indicators of adaptive capacity that determine agricultural vulnerability of the both study sites. Finally it is recommended that the intervention required for coastal adaptation of agriculture should be initiated by respective authorities.

Economic Analysis of Agricultural Credit on Production Efficiency of Borrower Farms

Credit is one of the most crucial but scarce inputs used in agriculture. Farm credit is an important instrument, which has been used to increase agricultural productivity. The main focus of this research is to examine the role of agricultural credits, production and efficiency of farms in Erode District of Tamil Nadu. Kavunthapadi and Modakurichi block of Erode district was selected. A complete enumeration of farm households which borrowed institutional crop loan in each of the sample villages was made. The survey was conducted among 60 borrowers and 45 non-borrowers farm households. In the present study, the efficiency of farms among borrower and non-borrower sample households was determined by the Stochastic Frontier production function of the Cobb- Douglas type had been used. And Tobit analysis was also done to know the effect of credit on farm efficiency. The efficiency scores obtained from first stage Stochastic Frontier Approach for borrower and non-borrower farms were used as dependent variable and a dummy variable to represent credit (X5i) were used as one of the independent variables in addition to other socio economic independent variables. Results of the model revealed that the number of borrower farms with a technical efficiency of more than 90 per cent were more (57 per cent of the total borrower farms) than that of non- borrower farms (33.3 per cent) which implies that the more percentage of farmers availed credit and adopted technology had higher technical efficiency level (90 per cent). The results also indicated that technical efficiency ranged from 0.41 to 0.99 for non borrowers and from 0.62 to 0.98 for borrowers. The results of Tobit regression analysis indicated that net operational area, farm experience, access to farm credit, had positive and significant relationship with the technical efficiency of the farmer.